A systematic review of automated methods to perform white matter tract segmentation

Abstract

White matter tract segmentation is a pivotal research area that leverages diffusion-weighted magnetic resonance imaging (dMRI) for the identification and mapping of individual white matter tracts and their trajectories. This study aims to provide a comprehensive systematic literature review on automated methods for white matter tract segmentation in brain dMRI scans. Articles on PubMed, ScienceDirect [NeuroImage, NeuroImage (Clinical), Medical Image Analysis], Scopus and IEEEXplore databases and Conference proceedings of Medical Imaging Computing and Computer Assisted Intervention Society (MICCAI) and International Symposium on Biomedical Imaging (ISBI), were searched in the range from January 2013 until September 2023. This systematic search and review identified 619 articles. Adhering to the specified search criteria using the query, “white matter tract segmentation OR fiber tract identification OR fiber bundle segmentation OR tractography dissection OR white matter parcellation OR tract segmentation,” 59 published studies were selected. Among these, 27% employed direct voxel-based methods, 25% applied streamline-based clustering methods, 20% used streamline-based classification methods, 14% implemented atlas-based methods, and 14% utilized hybrid approaches. The paper delves into the research gaps and challenges associated with each of these categories. Additionally, this review paper illuminates the most frequently utilized public datasets for tract segmentation along with their specific characteristics. Furthermore, it presents evaluation strategies and their key attributes. The review concludes with a detailed discussion of the challenges and future directions in this field.

1 Introduction

The development of diffusion magnetic resonance imaging (dMRI) coupled with the subsequent introduction of techniques to model water diffusion within the brain tissue using a diffusion tensor model (DTI) (Delmarcelle and Hesselink, 1992; Basser et al., 1994a, 1994b; Carter et al., 2015), has led to unprecedented opportunities for noninvasive exploration of the brain’s intricate white matter (WM) structures (Clayden et al., 2007). Tractography is a technique that harnesses data derived from dMRI to reconstruct and visualize the WM pathways within the brain by tracing the likely paths of water diffusion. Tractography (Mori et al., 1999; Basser et al., 2000) involves the algorithmic reconstruction of these WM pathways, generating a multitude of fibers (El Kouby et al., 2005) for each subject. This is followed by the delineation of the obtained fiber trajectories or streamlines into bundles or their association with anatomically well-defined tracts, a process commonly referred to as WM tract segmentation or dissection (Bullock et al., 2019).

WM tracts in the brain serve as the communication highways that connect different regions of the brain. Accurate segmentation enables researchers and clinicians to identify specific tracts associated with particular neurological functions, including cognitive, motor, and behavioral processes (Yushkevich et al., 2008; Sadeghi et al., 2013). Accurate tract segmentation plays a pivotal role in comprehending alterations in the micro- and macro-structure of the brain’s WM. It enhances our understanding of how structural connectivity shapes brain function and development. Additionally, it provides valuable insights into neurological diseases, including cognitive impairment and neurodegeneration, mental health disorders, and the aging process (Catani, 2006; De Belder et al., 2012; Le Bihan and Johansen-Berg, 2012). Moreover, accurate WM tract segmentation holds immense clinical significance, particularly in aiding in pre-operative and intra-operative brain tumor resections. It facilitates the visualization and localization of WM tracts that may be displaced or affected by tumors (Lazar et al., 2006; Chen et al., 2016; Essayed et al., 2017; Vanderweyen et al., 2020). It is worth noting that WM tract segmentation is a very challenging task. The human brain contains millions of intertwined axonal pathways, and these fibers can cross, split, or merge, making it challenging to accurately track individual pathways.

Most techniques employed for WM tract segmentation are based on virtual dissection or manual approaches, which involve the meticulous delineation of regions of interest (ROIs) (Catani et al., 2002; Mori and van Zijl, 2007; Wakana et al., 2007). These ROIs define where streamlines should pass and where streamlines should terminate. The provision of ROIs requires expert knowledge and hence manual methods incur expert labor costs. Manual methods face practical challenges in their adoption since they are time-consuming and expensive due to their high clinical and labor costs. Nevertheless, manual methods remain the gold standard for delineating WM tracts and serve as a critical benchmark for validating alternative approaches. The advent of better imaging techniques, improved image quality and higher resolutions (Van Essen et al., 2012), along with the application of sophisticated post-processing techniques, has driven a significant surge in the development of automated methods for tract segmentation (Yamada et al., 2009; Essayed et al., 2017; Ghazi et al., 2023).

A wide range of automated white matter tract segmentation methods have been developed over the years. While multiple works exist that review tractography methods and their applications, currently, there is limited literature available that specifically discusses the topic of delineating white matter tracts. Authors summarize the various categories that tractography segmentation methods fall under (Zhang et al., 2022) when reviewing quantitative tractography methods for studying the brain’s structural connectivity in health and disease. Recently, authors in Ghazi et al. (2023) have reviewed literature focusing on deep learning approaches for tract segmentation. In this work, we extend the scope by conducting a systematic and comprehensive review of automated approaches for the segmentation of white matter tracts in the last decade. This paper contributes to the following:

• Review of automated tract segmentation methods explored within the last 10 years with respect to key research questions.

• Identify the categories of methods and their research gaps and challenges.

• Highlight an overview of the various datasets and evaluation metrics used in the methods.

• Discuss the future directions that can be conducted.

The remainder of the survey is organized as follows: Section 2 presents the review planning, Section 3 introduces the key findings as results, Section 4 summarizes and discusses the findings, Section 5 outlines the future directions, and Section 6 concludes the review.

2 Review planning

This section is dedicated to planning the review: the comprehensive research questions related to the study are rigorously defined, the identification criteria and the resources of study are detailed.

2.1 Key research questions

• What method is developed?

• What dataset is used?

• What evaluation metrics are used?

• What category of method does the study fall under?

• Is the code for the automatic tract segmentation method publicly available, is the practical applicability of the method discussed in terms of computation time and external validation?

2.2 Sources of information

The sources of information listed below were searched between the time span from January 2013 until September 2023 using the query “white matter tract segmentation OR fiber tract identification OR fiber bundle segmentation OR tractography dissection OR white matter parcellation OR tract segmentation”

• Pubmed (https://pubmed.ncbi.nlm.nih.gov/)

• Science direct (https://www.sciencedirect.com) for publication titles under NeuroImage, NeuroImage: Clinical, and Medical Image Analysis

• Scopus (https://www.scopus.com/)

• IEEE explore digital library (https://ieeexplore.ieee.org/)

• Conference publications for: Medical Imaging Computing and Computer Assisted Intervention Society (MICCAI), International Symposium on Biomedical Imaging (ISBI)

2.3 Inclusion criteria

Inclusion requirements were: (a) original research article published in the selected journal publications of Pubmed, ScienceDirect, Scopus, IEEE Explore Digital library and conference publications MICCAI and ISBI; (b) published within the last 10 years from January 2013 until September 2023; (c) published in English; (d) performed automated white matter tract segmentation in human brains; and (e) research articles specifically developing automated methods for white matter tract segmentation performed on deep white matter. Search strings were established via literature search and domain expertise. Specifically, title and abstract articles were searched on each of the above-mentioned sources of information using strings: white matter tract segmentation OR fiber tract identification OR fiber bundle segmentation OR tractography dissection OR white matter parcellation OR tract segmentation.

3 Results

Our search strategy retrieved 619 articles published between January 2013 and September 2023. After articles were reviewed for definite exclusions and the bibliography of eligible articles were hand-searched, 59 articles met the inclusion criteria. Figure 1 shows the flow diagram of the retrieved articles and the rules applied to get the resulting 59 articles. The results are presented as follows: First, we summarize the major datasets used in the studies included in this review. We then provide a list of the 59 research articles by focusing on the research questions established. These research articles are mentioned according to the categories they belong to and finally we provide a summary for the evaluation metrics used by the studies.

Figure 1

3.1 Datasets

We present a list of the most commonly used imaging datasets used for the 59 studies. For each dataset we highlight the population details, the MRI acquisition details and online link to access the dataset. Table 1 lists the dataset studied.

3.2 Automated methods for white matter tract segmentation

All automated methods included in this review can be classified into categories based on the specific technique used for automatic tract segmentation. The high-level categories have been specified in Table 2, noting the references in which they were implemented. Figure 2 shows a bar graph of the distribution of studies within the categories. Some studies have used methods which have been developed as a combination of multiple categories and are referred to as a hybrid approach. The goal of this section is to investigate the findings corresponding to the questions framed in the review planning phase in Section 2. Tables 3–7 give a list of each of the studies and summarizes their inclusion criteria; the dataset used in the study, an overview of the approach used, the evaluation metrics used to validate the results in the work, and finally the practical application of the study in terms of public availability of the algorithm, the computational runtime to segment white matter tracts for a single subject and whether external validation has been conducted.

Table 3

Direct Voxel-based methods
Author/Year/Citation	Dataset/No. of Subjects	Main Context	Architecture	No. of tracts segmented	Data Augmentation	Performance Metrics	Practical Application
							Code	Runtime per subject	External validation
Liu, Wan, et al./2023/ (Liu et al., 2023)	HCP/105; Private/16	- Transfer knowledge of pretrained CNN using fine-tuning strategy for new tracts with only a single annotated scan - Use extensive data augmentation	2D U-net (Ronneberger et al., 2015)	12	Random Cutout, Tract Cutout	Dice: 0.619 ~ 0.693	N/A	N/A
Lucena, Oeslle, et al./2022/ (Lucena et al., 2022)	HCP/105	- Based on 3D nnUNet with raw dMRI intensities transformed into spherical harmonics (SH) space - Also output uncertainty measurement with respect to groundtruth	3D nnUNet (Isensee et al., 2021)	72	3D rotation to both the spatial location and SH coefficients	Dice: 0.82 Sensitivity: 0.85 ~ 0.86 Specificity: 0.78 ~ 0.80 ASSD: 0.63 ~ 0.66 Hausdorff distance: 9.24 ~ 10.57	https://github.com/OeslleLucena/TractSegmentation (Link inactive)	N/A
Lu, Qi, et al./2022/ (Lu et al., 2022)	HCP/100; Private/12	- Transfer knowledge of pretrained CNN using fine-tuning strategy for new tracts with only few annotated scans - Utilize data augmentation strategy for learning in few-shot setting	2D U-net (Ronneberger et al., 2015)	12	Mixing-based data augmentation (Zhang et al., 2017; Yun et al., 2019)	Dice: 0.780 ~ 0.846 Relative Volume Difference (RVD): 0.129 ~ 0.156	N/A	N/A
Liu, Wan, et al./2022/ (Liu et al., 2022)	HCP/100; Private/17	- Utilize tract correlation by embedding tract labels as a vector - Integrate label embedding with segmentation module built using TractSeg (Wasserthal et al., 2018)	2D U-net (Ronneberger et al., 2015)	72	Angular and spatial downsampling of dMRI	Dice: 0.582 ~ 0.851	https://github.com/liuwan0208/TractSegWithLabelEmbedding	N/A
Wang, Zhenwei, et al./2022/ (Wang et al., 2022)	HCP/205 3D Fiber atlas (Zhang et al., 2018)	- Represent the spatial distribution and shape of fibers using a novel descriptor called FiberGeoMap	Transformer (Vaswani et al., 2017)	103	N/A	Precision: 0.9279 Recall: 0.9478 Accuracy: 0.9319 Dice: 0.9268	https://github.com/Garand0o0/FiberTractSegmentation	N/A
Yin, Haoran, et al./2022/ (Yin et al., 2022)	HCP/105	- Utilized a modified U-net architecture to use a dense crisscross attention mechanism	CCNet (Huang et al., 2023)	72	Elastic Deformation, rotation, resampling, gaussian noise, displacement, zooming	Dice: 0.843	N/A	N/A
Lu, Qi, Yuxing Li, and Chuyang Ye/ 2021/ (Lu et al., 2021)	HCP/155	- Exploit self-supervised learning since pretext tasks do not require manual annotations - Transfer knowledge learned in pretraining using fine-tuning	2D U-net (Ronneberger et al., 2015)	72	Angular and spatial downsampling of dMRI	Dice: 0.813 RVD: 0.128	N/A	N/A
Li, Siqi, et al./2021/ (Li et al., 2021)	HCP/102	- Utilize fractional anisotropy (FA) images and T1 weighted images - combine output of two parallel architectures for final output	2D U-net (Ronneberger et al., 2015)	1	Cropping, Contrast augmentation, Brightness augmentation, Hue augmentation	Dice: 0.855	N/A	N/A
Lu, Qi, Yuxing Li, and Chuyang Ye/2020/ (Lu et al., 2020)	HCP/155	- Exploit self-supervised learning along with pseudo-labelling	2D U-net (Ronneberger et al., 2015)	72	N/A	Dice: 0.761 ~ 0.768	N/A	N/A
Li, Bo, et al./2020/ (Li et al., 2020)	Rotterdam Study/5286 Iris Study (Steketee et al., 2016)/−	- Utilize 4D diffusion tensor image directly as input - Separate network trained for each tract	3D U-net	25	N/A	Dice: 0.72 ~ 0.83	N/A	0.49 s
Nelkenbaum, Ilya, et al./2020/ (Nelkenbaum et al., 2020)	HCP/105	- Utilize both T1-weighted and principal direction of diffusion (PDD) images as input	VNet (Milletari et al., 2016)	14	Angular and spatial downsampling of dMRI	Dice: 0.722 ~ 0.869	N/A	N/A
Wasserthal, Jakob, et al./2019/ (Wasserthal et al., 2019)	HCP/105	- Built on top of TractSeg (Wasserthal et al., 2018) - Module for tract start and end segmentation added – Module for tract orientation mapping (TOM) prediction added	2D U-net (Ronneberger et al., 2015)	72	Rotation, Elastic deformation, Displacement, Zooming, Resampling, Gaussian noise	Dice: 0.74 ~ 0.85	https://github.com/MIC-DKFZ/TractSeg/	8.95 min
Pomiecko, Kristofer, et al./ 2019/ (Pomiecko et al., 2019)	Private/240	- Utilize whole brain MRI diffusion anisotropy maps as input - Separate network trained for each tract	Multi-scale 3D U-net based on DeepMedic (Kamnitsas et al., 2017)	12	N/A	Dice: 0.72	N/A	16 s
Dong, Xiaofeng, et al./2019/ (Dong et al., 2019)	HCP/105 Human Brain Data Sharing Initiative (HBDSI)/−	- Utilize both T1-weighted images and fiber orientation distribution function (fODF) as input	2D U-net (Ronneberger et al., 2015)	72	Edge enhancing diffusion filter	Dice: 0.832	N/A	N/A
Wasserthal, Jakob, Peter Neher, and Klaus H. Maier-Hein/ 2018/ (Wasserthal et al., 2018)	HCP/105	- Utilizes fiber orientation distribution function (fODF) peaks as input - Semi-automatically generated binary segmentations for 72 tracts made public	2D U-net (Ronneberger et al., 2015) named TractSeg	72	Rotation, Elastic deformation, Displacement, Zooming, Resampling, Gaussian noise, Contrast augmentation, Brightness augmentation	Dice: 0.82 ~ 0.84	https://github.com/MIC-DKFZ/TractSeg/	1 min
Ocegueda, Omar, and Mariano Rivera/2013/ (Ocegueda and Rivera, 2013)	2012 HARDI Reconstruction Challenge Dataset/− Phantom/−	- Represent DWI signal using a Muti-Tensor Field model - Points are embedded using eigenvectors to perform segmentation	Entropy-Controlled Quadratic Markov Measure Field (EC-QMMF)	16	N/A	Qualitative Results	N/A	14 min

Direct voxel-based methods for Automated White Matter Tract Segmentation.

Table 4

Streamline-based clustering methods
Author/Year/Citation	Dataset/No. of Subjects	Main Context	Clustering Algorithm	No. of fiber clusters	Performance Metrics	Practical Application
						Code	Runtime per subject	External Validation
Chen, Yuqian, et al./2023/ (Chen et al., 2023)	HCP/50; CNP/40; PPMI/30	- Trained using self-supervised learning with the pretext task of predicting pairwise fiber distances	K-Means; Deep Convolutional Embedded Clustering	800	Davies-Bouldin Index (DB): 2.014 ~ 2.119 White Matter Parcellation Generalization (WMPG): 0.970 ~ 0.996 Tract Anatomical Profile Coherence (TAPC): 0.830 ~ 0.844 Tract Surface Profile Coherence (TSPC): 0.476 ~ 0.601	https://github.com/SlicerDMRI/DFC	15 ~ 110 s
Zhao, Yi, et al./2022/ (Zhao et al., 2022)	HCP	- Multimodal dMRI and fMRI data (extracted BOLD signals) used as input for clustering	Riemannian metric geodesic distance to measure structural and functional differences for clustering fibers	72	Mean undirected euclidean distance (UE) Mean functional correlation (FC)	N/A	N/A
Chen, Yuqian, et al./2021/ (Chen et al., 2021)	HCP/200	- Based on self-supervised learning with the pretext task of pairwise fiber distance prediction	Siamese Networks, K-means, CNN	800	WMPG: 99.35% TAPC: 0.836	N/A	205 s
Xu, Chaoqing, et al./2021/ (Xu et al., 2021)	Private/−	- Based on encoding streamlines into 31 features and fed to encoder-decoder type architecture	Improved Deep Embedded Clustering (IDEC)	10	Qualitative results; Expert assessment	N/A	3 min
Logiraj, Kumaralingam, et al./2021/ (Logiraj et al., 2021a)	ADNI/20	- Based on geometrical curve features and multi-feature matching	Progressive clustering of large clusters of curves into smaller ones	6	Accuracy: 86% ~ 87%	N/A	N/A
Vazquez, Andrea, et al./2020/ (Vázquez et al., 2020)	HARDI ARCHI/50	- Based on refining and merging clusters	Fast Fiber Clustering (FFClust)	150–200	Davies Bouldin Index (DB): 0.7 ~ 0.75 Execution Time: 1.99 min for 1 subject with 1 million fibers and parallel 45 s.	https://github.com/andvazva/FFClust	9.92 s
Yang, Zhipeng, et al./2020/ (Yang et al., 2020)	Private/7	- Based on using multi-modal information by combining spatial features and fMRI signals in WM	Gaussian Mixture Model (GMM) and Expectation Maximization (EM)	48	Hausdorff distance: 4.1 ~ 48.4	N/A	N/A
Siless, Viviana, et al./2018/ (Siless et al., 2018)	HCP/32	- Based on a novel anatomical similarity measure	Normalized Cuts (Brun et al., 2004; Shi and Malik, 2000)	200	Dice: 0.55–0.60	N/A	2.45 ~ 2392.37 min
Gupta, Vikash, et al./2018/ (Gupta et al., 2018)	PPMI/226	- Use CNN to learn shape features and cluster streamlines	Convolutional Neural Network (CNN)	10	Accuracy: 97%	N/A	N/A
Gupta, Vikash, et al./2017/ (Gupta et al., 2017)	Private/42	- Use CNN to learn shape features and cluster streamlines	Convolutional Neural Network (CNN)	17	Qualitative results	N/A	N/A
Román, Claudio, et al./2017/ (Roman et al., 2017)	Private/74	- Use intersubject hierarchical clustering of fibers - Create an atlas of identified bundles to promote automatic labeling	Heirarchical clustering	93	Lateralization index (Catani et al., 2012): –0.171 ~ 0.389	N/A	2.6 ~ 3.4 h
Kamali, Tahereh, and Daniel Stashuk/2016/ (Kamali and Stashuk, 2016)	JHU DTI (http://lbam.med.jhmi.edu)/15	- Based on distances of nearest neighbors of individual fibers - separate high densities (smaller distances) from lower densities (higher distances)	Neighborhood Distance Entropy Consistency (NDEC)	3	Dice: 0.94 Density-Based Clustering Validation (DBCV): 0.71	N/A	2 min
Kumar, Kuldeep, and Christian Desrosiers /2016/ (Kumar and Desrosiers, 2016)	HCP/10	- Atlas created from multi-subject data by learning a compact dictionary of training fibers describing the whole dataset	Kernel Sparse Clustering (KSC)	4	0.634 ~ 0.809	N/A	0.876 ~ 2.736 s
Jin, Yan, and H. Ertan Cetingül/2015/(Jin and Cetingül, 2015)	Neurospin MR phantom dataset (Poupon et al., 2008)/65 HCP/10	- Group fibers growing from a manually selected ROI and monitor divergence of fibers through drift detection while tractography is performed	Affinity Propagation (AP) (Frey and Dueck, 2007)	5	Dice Coefficient: 0.91 ~ 1.0	N/A	2 min
Tunç, Birkan, et al./2014/ (Tunc et al., 2014)	Private/6	- Based on a connectivity-based representation of fibers - Also generate a fiber clustering atlas which is used for further clustering unknown subjects	Gaussian Mixture Model (GMM) (Reynolds et al., 2000) and Expectation Maximization (EM) (Dempster et al., 1977)	327	Dice: 0.62 ~ 0.93	N/A	N/A

Streamline-based clustering methods for Automated White Matter Tract Segmentation.

Table 5

Streamline-based classification methods
Author/Year/Citation	Dataset/No. of subjects	Main context	Architecture	No. of fiber clusters	Performance metrics	Practical application
						Code	Runtime per subject	External validation
Dumais, Félix, et al./2023/ (Dumais et al., 2023)	TractInferno (Poulin et al., 2022)/354; HCP/1200; MyeloInferno/45; ADNI/23; PPMI/34	- Based on training an autoencoder on contrastive loss using whole brain tractograms	AutoEncoder	27	Dice: 0.74 ± 0.08	https://github.com/scil-vital/fiesta	N/A
Bertò, Giulia, et al./2021/ (Bertò et al., 2021)	HCP-minor/105; HCP-IFOF/30; HCP-major/105; Private/10	- Based on vector representation using anatomical and geometrical information of streamlines	Linear Classifier	500	Dice: 0.80 ~ 0.91	https://brainlife.io	3 min
Logiraj, Kumaralingam, et al./2021/ (Logiraj et al., 2021b)	Private/15	- Based on segmenting 3D fiber curves into bundles	PointNet (Qi et al., 2017)	10	Accuracy: 97.06% Precision: 0.98 ~ 1.0 Recall: 0.91 ~ 1.0	N/A	N/A
Zhang, Fan, et al./2020/ (Zhang et al., 2020)	HCP/100; dHCP/40; ABCD/50; CNP/50; PPMI/50; BTP/39	- Based on fiber descriptor called FiberMap (Zhang et al., 2020)	2D CNN	54	Accuracy: 90.99% Recall: 85.67% Precision: 88.47% Tract Identification Rate: 99.17% ~ 99.96% Weighted Dice: 0.91 ~ 0.97	http://dmri.slicer.org	8 min
Wu, Ye, et al./2020/ (Wu et al., 2020)	HCP/105	- Based on representing each fiber bundle by compact dictionary	Dictionary Learning Tool DICTOL (Vu and Monga, 2017)	72	Accuracy: about 0.6–1.0	N/A	N/A
Zhang, Fan, et al./2019/(Zhang et al., 2019)	dHCP/40; ABIDE/70; HCP/100; CNP/204; PPI/144; BTP/39	- Based on fiber descriptor called FiberMap (Zhang et al., 2020)	2D CNN	54	Accuracy: 90.99% Recall: 85.67% Precision: 88.47% Tract Identification Rate: 99.17–100%	https://github.com/SlicerDMRI/DeepWMA	8 min
Liu, Feihong, et al./2019/ (Liu et al., 2019)	HCP/38	- Based on representing streamlines as graphs - Separate network trained for each bundle	Graph Convolutional Neural Network (GCNN)	12	Precision: 90.5% ~ 9.9% Recall: 88.4% ~ 100% Dice: 80.7 ~ 99.1	N/A	N/A
Ugurlu, Devran, et al./2019/(Ugurlu et al., 2019)	HCP/30	- Based on representing each streamline as the fiber orientation distributions in its neighborhood	NN	9	Bundle-based Minimum Distance (BMD): 1.2 ~ 5.46 Kappa: 0.68 ~ 0.84	N/A	N/A
Bertò, Giulia, et al. /2019/ (Bertò et al., 2019)	HCP/130	- example created based on 130 tractograms and using the Automated Fiber Quantification (Yeatman et al., 2012) algorithm	Linear assignment problem for segmentation and ROI-based distance matrix	12	Dice: 0.84 ~ 0.87	doi: 10.25663/brainlife.app.122	N/A
Lam, Prince D. Ngattai, et al./2018/ (Ngattai Lam et al., 2018)	Private/685	- Based on fiber features curvature, torsion and euclidean distances to a certain number of landmarks and CNN used to classify	2D NN	1	Accuracy: 98.8%	N/A	N/A
Heker, Michal, et al./2016/ (Heker et al., 2016)	HCP/15	- Based on Adaboost selected features such as fiber length, location, variance, etc.	Viola-Jones (Viola and Jones, 2001)	3	Dice: 0.90 ~ 0.91	N/A	N/A
Ratnarajah, Nagulan, and Anqi Qiu/2014/ (Ratnarajah and Qiu, 2014)	GUSTO study (Soh et al., 2014)/20	- Based on Riemannian structure of diffusion tensors	Multi-label k-NN	13	Hamming Loss: 0.041 ~ 0.053 One error: 0.098 ~ 0.200 Coverage: 0.104 ~ 0.181 Volume Overlap percentage: 0.764	N/A	N/A

Streamline-based classification methods for Automated White Matter Tract Segmentation.

Table 6

Atlas-Based Methods
Author/Year/Citation	Dataset/No. of Subjects	Main Context	Architecture	No. of fiber clusters	Performance Metrics	Practical Application
						Code	Runtime per subject	External Validation
Radwan, ahmed M, et al./2022/ (Radwan et al., 2022)	HCP/20; MASSIVE/1	- Builds an atlas based on literature-based dissection protocol - atlas applied to new subjects using registration	ANTs (Avants et al., 2009)	68	Weighted-Dice: 0.747 ~ 0.963	https://github.com/KUL-Radneuron/KUL_FWT.git, https://osf.io/snq2d/	N/A
Jordan, Kesshi M., et al./2021/ (Jordan et al., 2021)	UCSF Dyslexia Center/59	- FreeSurfer derived ROIs used for anatomical information - RecoBundles (Garyfallidis et al., 2018) used to filter out the streamlines that do not match the shape of the tract based on predefined 3D bundle templates	Streamline Linear Registration	6	Dice: 0.76	https://github.com/kesshijordan/Kesh_Autoseg_Tools/tree/v1.0.0	N/A
Vázquez, Andream et al./2019/ (Vázquez et al., 2019)	HARDI ARCHI/−	- Utilize Euclidean distance between subject fiber and atlas centroid using multi-subject bundle atlas	N/A	100/62 based on atlas used	Execution Time: 6 min	N/A	6 min
Zhang, Fan, et al./2018/ (Zhang et al., 2018)	HCP/200; dHCP/40; ABIDE/70; CNP/204; PPMI/144; BTP/26	- Atlas created based on data obtained across multiple populations and different scanners	Entropy-based tractography registration	256	White matter parcellation Generalization (WMPG): 92.28 ~ 100 Tract Anatomical Profile Coherence (TAPC): 0.626 ~ 0.783 Inter Subject Parcellation Variability (ISPV): 0.264 ~ 0.919	https://github.com/SlicerDMRI/whitematteranalysis	N/A
Sharmin, Nusrat, Emanuele Olivetti, and Paolo Avesani/2018/ (Sharmin et al., 2018)	HCP/30	- Based on finding corresponding streamlines across different tractograms formulated as a linear assignment problem (LAP)	FLIRT/FSL (Fischer and Modersitzki, 2003)	10	Dice: 0.40 ~ 0.80 Receiver Operating Characteristic (ROC): 0.75 ~ 0.90	https://github.com/FBK-NILab/LAP_tract_segmentation	2 min
Labra, Nicole, et al./2017/ (Labra et al., 2017)	HARDI ARCHI/	- Compare subject streamlines to multisubject bundle atlas based on distance metric	N/A	26	Execution Time: 9 million streamlines in less than 6 min	integrated with the Brain VISA/Connectomist software (Duclap et al., 2012)	1 ~ 6.5 s
Yoo, Sang Wook, et al./2015/ (Yoo et al., 2015)	NMR/12	- Based on searching the most similar tract group in example data - multiple example subjects used; final label chosen based on voting scheme	FLIRT/FSL (Fischer and Modersitzki, 2003)	7	Consistency: 96.1% Sensitivity: 89.5% ~ 91.0% False Discovery Rate (FDR): 14.2% ~ 14.9% Kappa Analysis: 0.87 ~ 0.88	N/A	53.1 s
Jin, Yan, et al./2013/ (Jin et al., 2013)	HARDI/86	- Based on incorporating information from multiple hand-labeled atlases	ANTs (Avants et al., 2009)	17	Dice: 0.90 ~ 1.0	N/A	N/A

Atlas-based methods for Automated White Matter Tract Segmentation.

Table 7

Hybrid Methods
Author/Year/Citation	Dataset/ No. of subjects	Hybrid algorithms		No. of fiber bundles	Performance metrics	Practical application
						Code	Runtime per subject	External validation
Xu, H., et al./2023/ (Xu et al., 2023)	HCP/105	Registration Deep learning-based registration (Balakrishnan et al., 2019)	Segmentation TractSeg (Wasserthal et al., 2018)	72	Dice: 73.01%	https://github.com/HaoXu0507/ISBI2023-One-Shot-WM-Tract-Segmentation	N/A
Peretzke, Robin, et al./2023/ (Peretzke et al., 2023)	HCP/21 Private/10	Semi-Automatic Based on an active learning pipeline by training a random forest classifier on a specific tract	Manual Unlabeled streamlines from whole brain tractogram are manually annotated	3	Dice: 0.73 ~ 0.90	https://github.com/MIC-DKFZ/MITK-Diffusion	N/A
Delmonte, Alessandro, et al./2019/ (Delmonte et al., 2019)	HCP/5	Semi-Automatic Representing the inherent inaccuracy of anatomical definitions using theory of fuzzy sets (Bloch, 2005)	Manual Model qualitative anatomical definitions, navigate through levels of resolution	2	Qualitative Results	https://github.com/CorentinMercier/FBTS	100 s
Garyfallidis, Eleftherios, et al./2018/ (Garyfallidis et al., 2018)	BIL&GIN diffusion data (Mazoyer et al., 2016)/60	Clustering Quickbundles (Garyfallidis et al., 2012)	Registration Streamline-based Linear Registration (SLR)	4	Jaccard index: 0.21 ~ 0.26 Accuracy: 0.99 ~ 1.0 Sensitivity: 0.68 ~ 0.92 Specificity: 1.0 Bundle Adjacency: 0.53 ~ 0.68	http://dipy.org	N/A
O’Donell, Lauren J./2017/ (O’Donnell et al., 2017)	HCP/10; Private/18	Atlas-based Atlas learned using groupwise-registration and spectral clustering	Registration tractography-based registration to atlas	800	Accuracy: 80% ~ 94%	https://github.com/SlicerDMRI/whitematteranalysis	2.5 h
Wassermann, Demian, et al. /2016/ (Wassermann et al., 2016)	Private/77	Semi-Automatic: - a novel query language based on a near to English textual syntax to construct a dictionary of anatomical definitions describing white matter tracts	Manual: - tract descriptions are written by the operator as text sentences	32	Kappa score: 0.71 ~ 0.90	https://demianw.github.com/tract_querier	N/A
Chekir, Amira, et al./2014/ (Chekir et al., 2014)	HARDI/3;	Clustering: Quickbundles (Garyfallidis et al., 2012)	Atlas-based: WMPM Type 2 Eve Atlas	13	Kappa analysis: 0.70 Quantitative Diffusivity Analysis (FA average correlation): 0.94	N/A	N/A
Wassermann, Demian, et al./2013/ (Wassermann et al., 2013)	Private/77	Semi-Automatic: - a novel query language based on a near to English textual syntax to construct a dictionary of anatomical definitions describing white matter tracts	Manual - careful syntactical definition of major white matter tracts in the human brain based on a neuroanatomist’s expert knowledge	37	Mean FA to detect tract changes specific to schizophrenia	https://demianw.github.com/tract_querier	N/A

Hybrid methods for Automated White Matter Tract Segmentation.

Figure 2

3.2.1 Direct voxel-based methods

This category of methods directly segments tracts based on the diffusion images without performing tractography as shown in Figure 3. These methods are fast and utilize deep learning or machine learning techniques like convolutional neural networks (CNNs) to improve segmentation accuracy. Direct segmentation helps in providing a simpler processing pipeline and reduces potential errors due intermediate steps like registration (Mancini et al., 2019). Voxel-based approaches can associate each voxel with multiple tracts which is useful since WM tracts are known to cross or overlap (Jeurissen et al., 2019). Recent advances in GPU-based algorithms reduce algorithm runtimes to several minutes due to their highly parallelizable implementations. Although learning-based techniques achieve very high segmentation performance and are fast, they require a large number of manually annotated training data. Manual annotations are labor intensive to obtain, time-consuming and are prone to inter-observer intra-observer or even inter training set variability. Deep learning models also fail to generalize well on unseen data if they are trained on scarce training scans. Table 3 provides a list of all studies that use direct voxel-based approaches.

Figure 3

3.2.2 Streamline-based clustering

Streamline based methods are those that are applied to streamlines derived from whole brain tractography outputs as shown in Figure 4. These streamlines can be clustered or classified into meaningful groups of fibers known as bundles in either supervised or unsupervised ways. The unsupervised approach usually called streamline clustering methods are a popular white matter tract segmentation method. Such methods divide the entire brain white matter into multiple white matter parcels based on some information about the streamlines. Several bundles can be found using clustering-based methods, and the tractography data can also be characterized by using these clusters and their centroids as representative data which is used for further analyses. One of the main steps after clustering is to assign a label to the clustering results. This is a crucial step since clustering methods are commonly criticized to provide no guarantee of obtaining anatomically meaningful tracts (Toga and Mazziotta, 2002). Therefore in many cases, prior knowledge is used for this purpose, for example, by using an ROI atlas to guide the identification (Logiraj et al., 2021a) or from labeling clusters of streamlines from multiple subjects also called as atlas creation (Yoo et al., 2015; Labra et al., 2017) or labeling clusters in a single subject (Garyfallidis et al., 2018). Recently, deep learning methods are also being used for clustering large tractography datasets (Zhang et al., 2020). One of the main limitations of such methods is the large size of tractography datasets which are composed of various tracts of different shapes, lengths, positions. The advent of improved dMRI techniques has resulted in increased size and complexity of datasets. Tractography datasets comprise up to more than 10 million tracts. This causes an increase in storage and memory challenges when clustering such large datasets. Table 4 provides a list of all studies that use streamline-based clustering approaches.

Figure 4

3.2.3 Streamline-based classification

The supervised approach of streamline-based methods involves streamline-based classification or labeling as shown in Figure 5. These methods assign an anatomical label to each individual streamline. This can be done by computing a pairwise distance of each streamline to a labeled streamline in a reference tract segmentation and then assigning a streamline label based on the closest reference tract (Bertò et al., 2021). Recently, fibers obtained after tractography are classified into tracts using a deep learning-based classifiers such as CNNs which are trained on selected fiber features. Similar to segmentation methods, while they are fast in assigning labels to fibers, they also require a large number of manually annotated training data and tend to face similar issues as segmentation methods. Table 5 provides a list of all studies that use streamline-based classification approaches for automated methods for white matter tract segmentation.

Figure 5

3.2.4 Atlas-based

In atlas-based methods, tracts are identified by automatic placement of ROIs by warping a brain ROI atlas (Cook et al., 2005) or using volumes of interest (Oishi et al., 2009) to automatically group fiber streamlines into anatomically defined tracts as shown in Figure 6. These methods also can be based on tract similarity, also called streamline-based methods, using pairwise tract distances with a reference streamline label and assign a label based on the reference label of the streamline it is closest to (O'Donnell and Westin, 2007; Wu et al., 2020). Such approaches require image-based multi-modal nonlinear registration so that the streamlines obtained from tractography, and the ROIs are in the same space. However, registration results are not perfect because aligning streamlines with ROIs is a challenging task and time-consuming and can be even more difficult when applied to pathological brains. While more tracts can be easily added to the reference streamline atlas, in such methods, limited quality of some tracts limits their generalization ability. Table 6 provides a list of all studies that use atlas-based approaches.

Figure 6

3.2.5 Hybrid

In this review we also identified methods that combined more than one strategy from the categories to extract more information to improve labeling of anatomical bundles. The semi-automated methods identified in this review are also included under this category. Semi-automated techniques typically involve human intervention, such as manual labeling or correction, within an otherwise automated process. These methods are more time consuming since they have multiple steps as compared to the other methods. Table 7 provides a list of all studies that use hybrid approaches.

3.3 Evaluation metrics

In this section we review the most common evaluation methods that have been used for validating the white matter tract segmentation results in the studies included in this work. Evaluation of accuracy for tract segmentation is difficult since the errors cannot point out which stage of the pipeline causes the issue; for example, it is difficult to determine whether the errors were generated from the preprocessing steps, the selected algorithm for tract segmentation, similarity metric, etc. Table 8 gives a list of the most frequently used evaluation metrics with the following attributes for each: the metric name, metric description provides a brief definition, the formulation of the metric to show how it is computed and finally the usage of the metric.

Other than these commonly used evaluation methods, white matter tract segmentation methods are also validated qualitatively in the form of visualizing the generated tract segmentation. Visualization by a domain expert is still used as a complementary method along with a few of the above-mentioned quantitative measures. Recently authors in Pujol et al. (2015) had initiated the DTI challenge to promote the standardized evaluation of tractography methods for neurosurgery. Despite ample research in the development of tractography and tract segmentation algorithms there is no consensus on the validation techniques to compare the different algorithms.

4 Discussion

In this review paper, we have provided a systematic review of automated methods for white matter tract segmentation with respect to the most widely used public datasets for this task, the various categories of automated methods developed, and the evaluation metrics used to study the performance of the method. Although there are studies that have reviewed automated methods for brain tractography (Poulin et al., 2019; Zhang et al., 2022) and also deep learning methods for tract segmentation (Ghazi et al., 2023), to the best of our knowledge, a systematic review that focuses on automated methods for tract segmentation has not been published yet. This review paper underscores the methodological advancements in building automated methods, as evidenced through the 59 articles included in this review.

While manual segmentation of tracts or virtual dissection methods were not a focus of this survey, multiple approaches have been proposed in the last decade that conduct fiber selection and anatomical labeling using expert knowledge (Rheault et al., 2020, 2022a,b; Ille et al., 2021). These methods focus on improving the design of white matter dissection protocols to build more generalizable and reproducible methods. In Schilling et al. (2021) authors show the need to have a standard nomenclature and definitions for white matter bundles and that there are still issues in tractography segmentation that need to be resolved so that they can be used in routine clinical settings. Such methods are worth mentioning in this survey since they show that segmenting white matter tracts is a crucial task and that there is still a lot of scope for improvement.

It is also important to note that our study of white matter tract segmentation focusses on fibers in the deep white matter. Multiple studies are available that investigate the segmentation of subcortical U-fibers, which are special types of short association fibers located in the superficial white matter (Guevara et al., 2017, 2020; Xue et al., 2023). Despite studies on superficial white matter (SWM) being sparse due to its complexity (Xue et al., 2023) have employed point-cloud-based deep learning techniques that concentrate on superficial white matter tract segmentation. Also, numerous studies on automated white matter tract segmentation methods were omitted from this review because they did not meet the search criteria used to compile the literature included in this study. For example, studies (Bazin et al., 2011; Yendiki et al., 2011) provide automated methods for white matter tract segmentation, however, were not included in this study since they are published before 2013. Studies centered on automated fiber tracking or tractography (Teeuw et al., 2015; Warrington et al., 2020) are not featured in this review; however, they merit attention as they play a vital role in advancing new automated methodologies for accurately reconstructing white matter pathways, thereby facilitating the analysis of extensive datasets. Authors in Warrington et al. (2020) present tractography protocols as a software tool for standardized and automated cross-species tractography generated from large datasets. Automated tractography methods such as TRACULA (Yendiki et al., 2011), Teeuw et al. (2015) have used learning-based methods that show highly promising performance incorporating information on the anatomy of the pathways for reconstruction of white matter pathways thereby facilitating automated fiber tracking to large studies.

Twenty-seven percent of the papers in the current survey are based on direct voxel-based segmentation methods. Our results show that papers based on fully convolutional networks are typically based on encoder-decoder architecture such as U-net (Ronneberger et al., 2015). These voxel-based methods are gaining popularity with the advent of new and efficient deep learning-based segmentation techniques. However most current studies still rely on U-net based architectures as the baseline model, and the popular segmentation architectures like those based on transformers (Dosovitskiy et al., 2020; Hatamizadeh et al., 2021; Cao et al., 2022) have not been applied to this domain yet. Deep learning methods also perform segmentation by either labeling the streamlines or directly labeling the voxels. In general, the progress seen in using deep learning methods for medical image segmentation tasks (Hatamizadeh et al., 2021; Xiao et al., 2023) has not yet been fully applied to white matter tract segmentation. This is mostly because it is more demanding to have manual annotations of white matter tracts than other brain anatomical structures. Also, while deep learning methods provide fast segmentations, their results can still be unsatisfactory, and are not robust to changes of bundle sizes, tracking methods and data quality (Bertò et al., 2021). This shows that the major challenges in using machine learning or deep learning methods for tract segmentation will require researchers to come up with more generalizable solutions, create and publish more annotated datasets, use other techniques like transfer learning, self-supervised learning to overcome the challenges of limited training samples for deep learning-based methods to gain clinical applicability.

Next, our results show 25% of the papers in the current survey applied streamline-based clustering methods and 20% used streamline-based classification methods for automatic tract segmentation. These methods focus on clustering large number of fiber trajectories or streamlines into clusters or fiber bundles. However, few of these methods attach labels to clusters, and the clusters must be assigned labels either manually or automatically by using a streamline atlas usually incorporated in the clustering process. Such methods also have to post-process their results in order to filter the bundles to exclude spurious tracts that are falsely included in the clustering results.

In all the methods seen in this work, only two studies were found which used registration-based methods for white matter tract segmentation (Garyfallidis et al., 2018; Jordan et al., 2021). In Wasserthal et al. (2018) authors compared their work with two registration-based methods for automatic tract segmentation, which usually involves using a tract atlas and registering it to the subject of interest which yields a binary mask for each tract in subject space. Fewer registration methods are likely employed due to the inaccuracies produced during the registration step, and the computational complexity needed. However recently, a lot of work has been done in using deep learning methods for image registration (Oliveira and Tavares, 2014; Fu et al., 2020) to overcome the challenges of traditional registration methods. For example, authors in Zhang et al. (2021) proposed a deep learning-based method for registration of dMRI images. This exemplifies the growing interest in applying such methods to tract segmentation. Currently, to the best of our knowledge, there are no studies that use deep learning-based registration techniques for the task of automated white matter tract segmentation.

It is interesting to note that there is limited existing work on automated methods for segmenting white matter bundles for the neonatal brain. In our survey we only found two papers (Ratnarajah and Qiu, 2014; Logiraj et al., 2021b) that correspond to this topic. This could mainly be because segmenting white matter structures is particularly difficult in the neonatal brain since it is undergoing a critical growing process along with cellular maturation such as myelination and synaptic pruning (Ratnarajah and Qiu, 2014). Existing methods rely mostly on fully manual segmentation for delineating white matter structures (Oishi et al., 2011) or are based on semi-automated techniques (Huang et al., 2006). In Oishi et al. (2011) authors developed an atlas-based segmentation based on image registration, which also needs manual expert assessment in order to delineate the required white matter structures. This work was developed almost a decade ago and there have been multiple automated segmentation techniques proposed since that have been successfully applied to adult’s brain as shown in this survey. Manual methods also suffer challenges of being time consuming and require prior anatomical knowledge to achieve reasonable accuracy and reproducibility.

Overall, we observe that automated tract segmentation algorithms follow varied methods for pre-processing, augmenting, and training their datasets and few methods use multi-site datasets. Even the techniques used to generate reference tracts are not the same across most of the methods. This makes it impossible to assess the true generalizability and reliability of the proposed methods (Poulin et al., 2022). This problem is also observed in manual segmentation methods where there is varied reproducibility for segmenting the same tracts among different experts or the inter-protocol agreement across protocols for various white matter pathways is poor as shown in (Schilling et al., 2021).

We observe that most studies included in this review do not provide computational time making it challenging to assess the practicality of these methods. In general, streamline-based methods typically require substantial memory for generating millions of streamlines per subject, whereas direct voxel-based methods can segment white matter tracts for a test subject in under a minute (Wasserthal et al., 2018). Out of 59 segmentation methods reviewed, only 18 have been validated on external datasets with varying scanners and acquisition parameters. This lack of generalizability testing may be due to the limited availability of publicly accessible tract segmentation datasets. Despite this, direct voxel-based methods can be used for data augmentation during training to simulate domain shifts in external datasets, potentially reducing the domain-shift impact.

Lastly, we have summarized the most common evaluation metrics used by tract segmentation methods to validate their results in Section 3.3. However, there is no consensus on the evaluation metrics used to compare the various proposed approaches. Due to the limitation of ground truth, most methods rely on reproducibility in terms of intra- and inter-rater as well as test–retest reproducibility (Zhang et al., 2019; Rheault et al., 2020, 2022b) and consistency of methods across different populations and acquisitions (Wasserthal et al., 2018, 2019), as validations points for identifying a good tract segmentation method.

5 Future directions

Although this paper reveals the advancements of automated methods for white matter tract segmentation, there is still a lack of a general standardized method that can be reliably used by clinicians. There is still limited consensus on the definition of tracts even among knowledgeable and experienced professionals who are concerned about the inter- and intra-user reproducibility with manual placement of ROIs (Zhang et al., 2010). This further complicates the methodology development and validation process. This suggests that there is a need for the development of more standardized approaches for validation tract segmentation results.

The recent work of authors of TractSeg (Wasserthal et al., 2018) enabled the distribution of manually labelled tracts to the community so that researchers could collaboratively share the segmented tracts by experts. This gave rise to the development of more generalized approaches towards white matter tract segmentation, which otherwise would not have been possible. This has set a particularly good example so that in the future, researchers can continue to enable the progress, development, and assessment of higher-quality automated methods through such public collaborations. There is still substantial room for future improvements in the domain of generating high quality ground truth via expert neuroanatomists.

Another important aspect to consider when developing automated methods is their computational cost. With the advent of improved imaging tools for the acquisition of data and increasing efficiency of computational resources, there is a critical need for building applications that can be clinically used. Moreover, there has been a significant surge in image sizes. A decade ago, state-of-the-art MR acquisitions typically featured MR images of human brains with voxel sizes of 2 × 2 × 2 mm³. Today, we routinely encounter voxel sizes smaller than 1 × 1 × 1 mm³, as seen in data collected by projects like the Human Connectome Project (Van Essen et al., 2012; Glasser et al., 2016). Therefore, rapid tract segmentation approaches are needed to allow interactive analysis and also to efficiently handle very large imaging studies in a time and cost-effective manner.

Another important future direction would be to consider tractogram data generated from varied tracking algorithms as input to the automated tract segmentation methods developed. This is because a variety of tracking algorithms with different parameter values can be used by tractography studies to generate the tractograms. Then the segmentation of tracts could be applied to any of these generated tractograms, and the method should be able to adapt to all these diverse types of inputs.

6 Conclusion

This systematic review summarized 59 relevant articles in all. Unlike previous studies, our work focuses on a systematic review of methods for automated white matter tract segmentation developed in the last decade. This work framed crucial research questions to explain what approaches have been used for automated tract segmentation methods, discover key research gaps, determine datasets that are publicly accessible for researchers and summarize the most common evaluation techniques utilized. The literature published in this area as displayed and characterized in the Results section is one that is of growing and global interest.

References

• 1

  Avants B. B. Tustison N. Song G. (2009). Advanced normalization tools (ANTS). Insight J.2, 1–35.

  • Google Scholar

• 2

  Balakrishnan G. Zhao A. Sabuncu M. R. Guttag J. Dalca A. V. (2019). VoxelMorph: a learning framework for deformable medical image registration. IEEE Trans. Med. Imaging38, 1788–1800. doi: 10.1109/TMI.2019.2897538

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 3

  Basser P. J. Mattiello J. LeBihan D. (1994a). Estimation of the effective self-diffusion tensor from the NMR spin echo. J. Magn. Reson. B103, 247–254. doi: 10.1006/jmrb.1994.1037

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 4

  Basser P. J. Mattiello J. LeBihan D. (1994b). MR diffusion tensor spectroscopy and imaging. Biophys. J.66, 259–267. doi: 10.1016/S0006-3495(94)80775-1

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 5

  Basser P. J. Pajevic S. Pierpaoli C. Duda J. Aldroubi A. (2000). In vivo fiber tractography using DT-MRI data. Magn. Reson. Med.44, 625–632. doi: 10.1002/1522-2594(200010)44:4<625::AID-MRM17>3.0.CO;2-O

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 6

  Bazin P. L. Ye C. Bogovic J. A. Shiee N. Reich D. S. Prince J. L. et al . (2011). Direct segmentation of the major white matter tracts in diffusion tensor images. NeuroImage58, 458–468. doi: 10.1016/j.neuroimage.2011.06.020

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 7

  Bertò G. Avesani P. Pestilli F. Bullock D. Caron B. Olivetti E. (2019). Anatomically-informed multiple linear assignment problems for white matter bundle segmentation. 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019),

  • Google Scholar

• 8

  Bertò G. Bullock D. Astolfi P. Hayashi S. Zigiotto L. Annicchiarico L. et al . (2021). Classifyber, a robust streamline-based linear classifier for white matter bundle segmentation. NeuroImage224:117402. doi: 10.1016/j.neuroimage.2020.117402

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 9

  Bloch I. (2005). Fuzzy spatial relationships for image processing and interpretation: a review. Image Vis. Comput.23, 89–110. doi: 10.1016/j.imavis.2004.06.013

  • CrossRef
  • Google Scholar

• 10

  Brun A. Knutsson H. Park H.-J. Shenton M. E. Westin C.-F. (2004). Clustering fiber traces using normalized cuts. Medical Image Computing and Computer-Assisted Intervention–MICCAI 2004: 7th International Conference, Saint-Malo, France, September 26–29, 2004. Proceedings, Part I 7

  • Google Scholar

• 11

  Bullock D. Takemura H. Caiafa C. F. Kitchell L. McPherson B. Caron B. et al . (2019). Associative white matter connecting the dorsal and ventral posterior human cortex. Brain Struct. Funct.224, 2631–2660. doi: 10.1007/s00429-019-01907-8

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 12

  Cao H. Wang Y. Chen J. Jiang D. Zhang X. Tian Q. et al . (2022). Swin-unet: Unet-like pure transformer for medical image segmentation. European conference on computer vision

  • Google Scholar

• 13

  Carter M. Jennifer S. Farra N. Harris G. ScienceDirect (2015). Guide to research techniques in neuroscience. 2nd Edn Academic Press.

  • Google Scholar

• 14

  Catani M. (2006). Diffusion tensor magnetic resonance imaging tractography in cognitive disorders. Curr. Opin. Neurol.19, 599–606. doi: 10.1097/01.wco.0000247610.44106.3f

  • CrossRef
  • Google Scholar

• 15

  Catani M. Dell'acqua F. Vergani F. Malik F. Hodge H. Roy P. et al . (2012). Short frontal lobe connections of the human brain. Cortex48, 273–291. doi: 10.1016/j.cortex.2011.12.001

  • CrossRef
  • Google Scholar

• 16

  Catani M. Howard R. J. Pajevic S. Jones D. K. (2002). Virtual in vivo interactive dissection of white matter fasciculi in the human brain. NeuroImage17, 77–94. doi: 10.1006/nimg.2002.1136

  • CrossRef
  • Google Scholar

• 17

  Chekir A. Descoteaux M. Garyfallidis E. Côté M.-A. Boumghar F. O. (2014). A hybrid approach for optimal automatic segmentation of white matter tracts in hardi. 2014 IEEE Conference on Biomedical Engineering and Sciences (IECBES)

  • Google Scholar

• 18

  Chen Z. Tie Y. Olubiyi O. Zhang F. Mehrtash A. Rigolo L. et al . (2016). Corticospinal tract modeling for neurosurgical planning by tracking through regions of peritumoral edema and crossing fibers using two-tensor unscented Kalman filter tractography. Int. J. Comput. Assist. Radiol. Surg.11, 1475–1486. doi: 10.1007/s11548-015-1344-5

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 19

  Chen X. Udupa J. K. Bagci U. Zhuge Y. Yao J. (2012). Medical image segmentation by combining graph cuts and oriented active appearance models. IEEE Trans. Image Process.21, 2035–2046. doi: 10.1109/TIP.2012.2186306

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 20

  Chen Y. Zhang C. Song Y. Makris N. Rathi Y. Cai W. et al . (2021). Deep fiber clustering: anatomically informed unsupervised deep learning for fast and effective white matter parcellation. International Conference on Medical Image Computing and Computer-Assisted Intervention

  • Google Scholar

• 21

  Chen Y. Zhang C. Xue T. Song Y. Makris N. Rathi Y. et al . (2023). Deep fiber clustering: anatomically informed fiber clustering with self-supervised deep learning for fast and effective tractography parcellation. NeuroImage273:120086. doi: 10.1016/j.neuroimage.2023.120086

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 22

  Clayden J. D. Storkey A. J. Bastin M. E. (2007). A probabilistic model-based approach to consistent white matter tract segmentation. IEEE Trans. Med. Imaging26, 1555–1561. doi: 10.1109/TMI.2007.905826

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 23

  Cook P. A. Zhang H. Avants B. B. Yushkevich P. Alexander D. C. Gee J. C. et al .. (2005). An automated approach to connectivity-based partitioning of brain structures. Medical image computing and computer-assisted intervention–MICCAI 2005: 8th International Conference, Palm Springs, CA, USA, October 26–29, 2005, Proceedings, Part I 8

  • Google Scholar

• 24

  Davies D. L. Bouldin D. W. (1979). A cluster separation measure. IEEE Transac. Pattern Analysis Machine PAMI-1, 224–227. doi: 10.1109/TPAMI.1979.4766909

  • CrossRef
  • Google Scholar

• 25

  De Belder F. E. Oot A. R. Van Hecke W. Venstermans C. Menovsky T. Van Marck V. et al . (2012). Diffusion tensor imaging provides an insight into the microstructure of meningiomas, high-grade gliomas, and peritumoral edema. J. Comput. Assist. Tomogr.36, 577–582. doi: 10.1097/RCT.0b013e318261e913

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 26

  Delmarcelle T. Hesselink L. (1992). Visualization of second order tensor fields and matrix data. In Proceedings Visualization’92. (pp. 316–317). IEEE Computer Society.

  • Google Scholar

• 27

  Delmonte A. Mercier C. Pallud J. Bloch I. Gori P. (2019). White matter multi-resolution segmentation using fuzzy set theory. 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)

  • Google Scholar

• 28

  Dempster A. P. Laird N. M. Rubin D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. J. Royal Statistic. Soc.39, 1–22.

  • Google Scholar

• 29

  Di Martino A. O’connor D. Chen B. Alaerts K. Anderson J. S. Assaf M. et al . (2017). Enhancing studies of the connectome in autism using the autism brain imaging data exchange II. Scientific Data4, 1–15. doi: 10.1038/sdata.2017.10

  • CrossRef
  • Google Scholar

• 30

  Dong X. Peng J. Yang Z. Wu X. (2019). Multimodality white matter tract segmentation using CNN.

  • Google Scholar

• 31

  Dosovitskiy A. Beyer L. Kolesnikov A. Weissenborn D. Zhai X. Unterthiner T. et al . (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv

  • Google Scholar

• 32

  Duclap D. Lebois A. Schmitt B. Riff O. Guevara P. Marrakchi-Kacem L. et al . (2012). Connectomist-2.0: a novel diffusion analysis toolbox for BrainVISA. In Proceedings of the 29th ESMRMB meeting

  • Google Scholar

• 33

  Dumais F. Legarreta J. H. Lemaire C. Poulin P. Rheault F. Petit L. et al . (2023). FIESTA: autoencoders for accurate fiber segmentation in tractography. NeuroImage279:120288. doi: 10.1016/j.neuroimage.2023.120288

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 34

  El Kouby V. Cointepas Y. Poupon C. Riviere D. Golestani N. Poline J. B. et al . (2005). MR diffusion-based inference of a fiber bundle model from a population of subjects. Med. Image Comput. Comput. Assist. Interv.8, 196–204. doi: 10.1007/11566465_25

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 35

  Essayed W. I. Zhang F. Unadkat P. Cosgrove G. R. Golby A. J. O'Donnell L. J. (2017). White matter tractography for neurosurgical planning: a topography-based review of the current state of the art. NeuroImage15, 659–672. doi: 10.1016/j.nicl.2017.06.011

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 36

  Fischer B. Modersitzki J. (2003). FLIRT: A flexible image registration toolbox. International workshop on biomedical image registration,

  • Google Scholar

• 37

  Frey B. J. Dueck D. (2007). Clustering by passing messages between data points. Science315, 972–976. doi: 10.1126/science.1136800

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 38

  Froeling M. Tax C. M. Vos S. B. Luijten P. R. Leemans A. (2017). “MASSIVE” brain dataset: multiple acquisitions for standardization of structural imaging validation and evaluation. Magn. Reson. Med.77, 1797–1809. doi: 10.1002/mrm.26259

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 39

  Fu Y. Lei Y. Wang T. Curran W. J. Liu T. Yang X. (2020). Deep learning in medical image registration: a review. Phys. Med. Biol.65:20TR01. doi: 10.1088/1361-6560/ab843e

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 40

  Garyfallidis E. Brett M. Correia M. M. Williams G. B. Nimmo-Smith I. (2012). QuickBundles, a method for Tractography simplification. Front. Neurosci.6:175. doi: 10.3389/fnins.2012.00175

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 41

  Garyfallidis E. Côté M.-A. Rheault F. Sidhu J. Hau J. Petit L. et al . (2018). Recognition of white matter bundles using local and global streamline-based registration and clustering. NeuroImage170, 283–295. doi: 10.1016/j.neuroimage.2017.07.015

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 42

  Ghazi N. Aarabi M. H. Soltanian-Zadeh H. (2023). Deep learning methods for identification of white matter Fiber tracts: review of state-of-the-art and future prospective. Neuroinformatics21, 517–548. doi: 10.1007/s12021-023-09636-4

  • CrossRef
  • Google Scholar

• 43

  Glasser M. F. Smith S. M. Marcus D. S. Andersson J. L. Auerbach E. J. Behrens T. E. et al . (2016). The human connectome project's neuroimaging approach. Nat. Neurosci.19, 1175–1187. doi: 10.1038/nn.4361

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 44

  Guevara M. Guevara P. Roman C. Mangin J. F. (2020). Superficial white matter: a review on the dMRI analysis methods and applications. NeuroImage212:116673. doi: 10.1016/j.neuroimage.2020.116673

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 45

  Guevara M. Roman C. Houenou J. Duclap D. Poupon C. Mangin J. F. et al . (2017). Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography. NeuroImage147, 703–725. doi: 10.1016/j.neuroimage.2016.11.066

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 46

  Gupta V. Thomopoulos S. I. Corbin C. K. Rashid F. Thompson P. M. (2018). Fibernet 2.0: an automatic neural network based tool for clustering white matter fibers in the brain. 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018)

  • Google Scholar

• 47

  Gupta V. Thomopoulos S. I. Rashid F. M. Thompson P. M . (2017). FiberNET: an ensemble deep learning framework for clustering white matter fibers. Medical image computing and computer assisted intervention− MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11–13, 2017, Proceedings, Part I 20

  • Google Scholar

• 48

  Hatamizadeh A. Nath V. Tang Y. Yang D. Roth H. R. Xu D. (2021). Swin unetr: Swin transformers for semantic segmentation of brain tumors in MRI images (pp. 272–284.) Cham: Springer International Publishing.

  • Google Scholar

• 49

  Heker M. Amer R. Alexandroni G. Greenspan H. (2016). Automated supervised segmentation of anatomical fiber tracts using an AdaBoost framework. 2016 IEEE International Conference on the Science of Electrical Engineering (ICSEE)

  • Google Scholar

• 50

  Hofman A. Brusselle G. G. Murad S. D. van Duijn C. M. Franco O. H. Goedegebure A. et al . (2015). The Rotterdam study: 2016 objectives and design update. Eur. J. Epidemiol.30, 661–708. doi: 10.1007/s10654-015-0082-x

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 51

  Huang Z. Wang X. Wei Y. Huang L. Shi H. Liu W. et al . (2023). CCNet: Criss-cross attention for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell.45, 6896–6908. doi: 10.1109/TPAMI.2020.3007032

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 52

  Huang H. Zhang J. Wakana S. Zhang W. Ren T. Richards L. J. et al . (2006). White and gray matter development in human fetal, newborn and pediatric brains. NeuroImage33, 27–38. doi: 10.1016/j.neuroimage.2006.06.009

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 53

  Ille S. Ohlerth A.-K. Colle D. Colle H. Dragoy O. Goodden J. et al . (2021). Augmented reality for the virtual dissection of white matter pathways. Acta Neurochir.163, 895–903. doi: 10.1007/s00701-020-04545-w

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 54

  Isensee F. Jaeger P. F. Kohl S. A. A. Petersen J. Maier-Hein K. H. (2021). nnU-net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods18, 203–211. doi: 10.1038/s41592-020-01008-z

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 55

  Jeurissen B. Descoteaux M. Mori S. Leemans A. (2019). Diffusion MRI fiber tractography of the brain. NMR Biomed.32:e3785. doi: 10.1002/nbm.3785

  • CrossRef
  • Google Scholar

• 56

  Jin Y. Cetingül H. E. (2015). Tractography-embedded white matter stream clustering. 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI)

  • Google Scholar

• 57

  Jin Y. Shi Y. Zhan L. De Zubicaray G. I. McMahon K. L. Martin N. G. et al . (2013). Labeling white matter tracts in HARDI by fusing multiple tract atlases with applications to genetics. 2013 IEEE 10th International Symposium on Biomedical Imaging

  • Google Scholar

• 58

  Jordan K. M. Lauricella M. Licata A. E. Sacco S. Asteggiano C. Wang C. et al . (2021). Cortically constrained shape recognition: automated white matter tract segmentation validated in the pediatric brain. J. Neuroimaging31, 758–772. doi: 10.1111/jon.12854

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 59

  Kamali T. Stashuk D. (2016). Automated segmentation of white matter fiber bundles using diffusion tensor imaging data and a new density based clustering algorithm. Artif. Intell. Med.73, 14–22. doi: 10.1016/j.artmed.2016.09.003

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 60

  Kamnitsas K. Ledig C. Newcombe V. F. Simpson J. P. Kane A. D. Menon D. K. et al . (2017). Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Med. Image Anal.36, 61–78. doi: 10.1016/j.media.2016.10.004

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 61

  Kumar K. Desrosiers C. (2016). A sparse coding approach for the efficient representation and segmentation of white matter fibers. 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI)

  • Google Scholar

• 62

  Labra N. Guevara P. Duclap D. Houenou J. Poupon C. Mangin J.-F. et al . (2017). Fast automatic segmentation of white matter streamlines based on a multi-subject bundle atlas. Neuroinformatics15, 71–86. doi: 10.1007/s12021-016-9316-7

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 63

  Lacante M. Van Esbroeck R. De Vos A. (2008). Met een dynamische keuzebegeleiding naar een effectieve keuzebekwaamheid: Eindrapport OBPWO projecten 04.01 & 02.02 en Ministerieel Initiatief. Brussel/Leuven: Vrije Universiteit Brussel/Katholieke Universiteit Leuven.

  • Google Scholar

• 64

  Lazar M. Alexander A. Thottakara P. Badie B. Field A. (2006). White matter reorganization after surgical resection of brain tumors and vascular malformations. Am. J. Neuroradiol.27, 1258–1271. PMID:

  • Pubmed Abstract
  • Google Scholar

• 65

  Le Bihan D. Johansen-Berg H. (2012). Diffusion MRI at 25: exploring brain tissue structure and function. NeuroImage61, 324–341. doi: 10.1016/j.neuroimage.2011.11.006

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 66

  Li S. Chen Z. Guo W. Zeng Q. Feng Y. (2021) Two parallel stages deep learning network for anterior visual pathway segmentation. Computational Diffusion MRI, International MICCAI Workshop: Lima, Peru

  • Google Scholar

• 67

  Li B. De Groot M. Steketee R. M. Meijboom R. Smits M. Vernooij M. W. et al . (2020). Neuro4Neuro: a neural network approach for neural tract segmentation using large-scale population-based diffusion imaging. NeuroImage218:116993. doi: 10.1016/j.neuroimage.2020.116993

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 68

  Liu F. Feng J. Chen G. Wu Y. Hong Y. Yap P. T. et al . (2019). DeepBundle: Fiber bundle Parcellation with graph convolution neural networks. Graph. Learn. Med. Imaging11849, 88–95. doi: 10.1007/978-3-030-35817-4_11

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 69

  Liu W. Lu Q. Zhuo Z. Li Y. Duan Y. Yu P. et al . (2022). Volumetric segmentation of white matter tracts with label embedding. NeuroImage250:118934. doi: 10.1016/j.neuroimage.2022.118934

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 70

  Liu W. Zhuo Z. Liu Y. Ye C. (2023). One-shot segmentation of novel white matter tracts via extensive data augmentation and adaptive knowledge transfer. Med. Image Anal.90:102968. doi: 10.1016/j.media.2023.102968

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 71

  Logiraj K. Sotheeswaran S. Jeyasuthan M. Ratnarajah N. (2021a). Clustering of major white matter bundles using tract-specific geometric curve features. 2021 10th International Conference on Information and Automation for Sustainability (ICIAfS)

  • Google Scholar

• 72

  Logiraj K. Thanikasalam K. Sotheeswaran S. Ratnarajah N. (2021b). TractNet: a deep learning approach on 3D curves for segmenting white matter fibre bundles. 2021 21st International Conference on Advances in ICT for Emerging Regions (ICter)

  • Google Scholar

• 73

  Lu Q. Li Y. Ye C . (2020). White matter tract segmentation with self-supervised learning. Medical image computing and computer assisted intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part VII 23

  • Google Scholar

• 74

  Lu Q. Li Y. Ye C. (2021). Volumetric white matter tract segmentation with nested self-supervised learning using sequential pretext tasks. Med. Image Anal.72:102094. doi: 10.1016/j.media.2021.102094

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 75

  Lu Q. Liu W. Zhuo Z. Li Y. Duan Y. Yu P. et al . (2022). A transfer learning approach to few-shot segmentation of novel white matter tracts. Med. Image Anal.79:102454. doi: 10.1016/j.media.2022.102454

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 76

  Lucena O. Borges P. Cardoso J. Ashkan K. Sparks R. Ourselin S. (2022). Informative and reliable tract segmentation for preoperative planning. Front. Radiol.2:866974. doi: 10.3389/fradi.2022.866974

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 77

  Makropoulos A. Robinson E. C. Schuh A. Wright R. Fitzgibbon S. Bozek J. et al . (2018). The developing human connectome project: a minimal processing pipeline for neonatal cortical surface reconstruction. NeuroImage173, 88–112. doi: 10.1016/j.neuroimage.2018.01.054

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 78

  Mancini M. Vos S. B. Vakharia V. N. O'Keeffe A. G. Trimmel K. Barkhof F. et al . (2019). Automated fiber tract reconstruction for surgery planning: extensive validation in language-related white matter tracts. Neuroimage Clin.23:101883. doi: 10.1016/j.nicl.2019.101883

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 79

  Marek K. Jennings D. Lasch S. Siderowf A. Tanner C. Simuni T. et al . (2011). The Parkinson progression marker initiative (PPMI). Prog. Neurobiol.95, 629–635. doi: 10.1016/j.pneurobio.2011.09.005

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 80

  Mazoyer B. Mellet E. Perchey G. Zago L. Crivello F. Jobard G. et al . (2016). BIL&GIN: a neuroimaging, cognitive, behavioral, and genetic database for the study of human brain lateralization. NeuroImage124, 1225–1231. doi: 10.1016/j.neuroimage.2015.02.071

  • CrossRef
  • Google Scholar

• 81

  Milletari F. Navab N. Ahmadi S.-A. (2016). V-net: fully convolutional neural networks for volumetric medical image segmentation. 2016 fourth international conference on 3D vision (3DV),

  • Google Scholar

• 82

  Mori S. Crain B. J. Chacko V. P. Van Zijl P. C. (1999). Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging. Ann. Neurol.45, 265–269. doi: 10.1002/1531-8249(199902)45:2<265::AID-ANA21>3.0.CO;2-3

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 83

  Mori S. van Zijl P. (2007). Human white matter atlas. Am. J. Psychiatry164:1005. doi: 10.1176/ajp.2007.164.7.1005

  • CrossRef
  • Google Scholar

• 84

  Moulavi D. Jaskowiak P. A. Campello R. J. Zimek A. Sander J. (2014). Density-based clustering validation. Proceedings of the 2014 SIAM international conference on data mining

  • Google Scholar

• 85

  Nelkenbaum I. Tsarfaty G. Kiryati N. Konen E. Mayer A. (2020). Automatic segmentation of white matter tracts using multiple brain MRI sequences. 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI)

  • Google Scholar

• 86

  Ngattai Lam P. D. Belhomme G. Ferrall J. Patterson B. Styner M. Prieto J. C. (2018). TRAFIC: Fiber tract classification using deep learning. Proc. SPIE Int. Soc. Opt. Eng.10574:1057412. doi: 10.1117/12.2293931

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 87

  O’Donnell L. J. Suter Y. Rigolo L. Kahali P. Zhang F. Norton I. et al . (2017). Automated white matter fiber tract identification in patients with brain tumors. NeuroImage13, 138–153. doi: 10.1016/j.nicl.2016.11.023

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 88

  Ocegueda O. Rivera M. (2013). Multi-tensor Field spectral segmentation for white matter fiber bundle classification. 2013 IEEE 10th International Symposium on Biomedical Imaging

  • Google Scholar

• 89

  O'Donnell L. J. Westin C.-F. (2007). Automatic tractography segmentation using a high-dimensional white matter atlas. IEEE Trans. Med. Imaging26, 1562–1575. doi: 10.1109/TMI.2007.906785

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 90

  Oishi K. Faria A. Jiang H. Li X. Akhter K. Zhang J. et al . (2009). Atlas-based whole brain white matter analysis using large deformation diffeomorphic metric mapping: application to normal elderly and Alzheimer's disease participants. NeuroImage46, 486–499. doi: 10.1016/j.neuroimage.2009.01.002

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 91

  Oishi K. Mori S. Donohue P. K. Ernst T. Anderson L. Buchthal S. et al . (2011). Multi-contrast human neonatal brain atlas: application to normal neonate development analysis. NeuroImage56, 8–20. doi: 10.1016/j.neuroimage.2011.01.051

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 92

  Oliveira F. P. Tavares J. M. R. (2014). Medical image registration: a review. Comput. Methods Biomech. Biomed. Engin.17, 73–93. doi: 10.1080/10255842.2012.670855

  • CrossRef
  • Google Scholar

• 93

  Peretzke R. Maier-Hein K. H. Bohn J. Kirchhoff Y. Roy S. Oberli-Palma S. et al . (2023). atTRACTive: semi-automatic white matter tract segmentation using active learning. International Conference on Medical Image Computing and Computer-Assisted Intervention

  • Google Scholar

• 94

  Poldrack R. A. Congdon E. Triplett W. Gorgolewski K. Karlsgodt K. Mumford J. et al . (2016). A phenome-wide examination of neural and cognitive function. Scientific Data3, 1–12. doi: 10.1038/sdata.2016.110

  • CrossRef
  • Google Scholar

• 95

  Pomiecko K. Sestili C. Fissell K. Pathak S. Okonkwo D. Schneider W. (2019). 3D convolutional neural network segmentation of white matter tract masks from MR diffusion anisotropy maps. 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)

  • Google Scholar

• 96

  Poulin P. Jorgens D. Jodoin P. M. Descoteaux M. (2019). Tractography and machine learning: current state and open challenges. Magn. Reson. Imaging64, 37–48. doi: 10.1016/j.mri.2019.04.013

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 97

  Poulin P. Theaud G. Rheault F. St-Onge E. Bore A. Renauld E. et al . (2022). TractoInferno-A large-scale, open-source, multi-site database for machine learning dMRI tractography. Scientific Data9:725. doi: 10.1038/s41597-022-01833-1

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 98

  Poupon C. Rieul B. Kezele I. Perrin M. Poupon F. Mangin J. F. (2008). New diffusion phantoms dedicated to the study and validation of high-angular-resolution diffusion imaging (HARDI) models. Magnet. Reson. Med60, 1276–1283. doi: 10.1002/mrm.21789

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 99

  Pujol S. Wells W. Pierpaoli C. Brun C. Gee J. Cheng G. et al . (2015). The DTI challenge: toward standardized evaluation of diffusion tensor imaging Tractography for neurosurgery. J. Neuroimaging25, 875–882. doi: 10.1111/jon.12283

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 100

  Qi C. R. Su H. Mo K. Guibas L. J. (2017). Pointnet: deep learning on point sets for 3d classification and segmentation. Proceedings of the IEEE conference on computer vision and pattern recognition

  • Google Scholar

• 101

  Radwan A. M. Sunaert S. Schilling K. Descoteaux M. Landman B. A. Vandenbulcke M. et al . (2022). An atlas of white matter anatomy, its variability, and reproducibility based on constrained spherical deconvolution of diffusion MRI. NeuroImage254:119029. doi: 10.1016/j.neuroimage.2022.119029

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 102

  Ratnarajah N. Qiu A. (2014). Multi-label segmentation of white matter structures: application to neonatal brains. NeuroImage102, 913–922. doi: 10.1016/j.neuroimage.2014.08.001

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 103

  Reynolds D. A. Quatieri T. F. Dunn R. B. (2000). Speaker verification using adapted Gaussian mixture models. Digit. Signal Process.10, 19–41. doi: 10.1006/dspr.1999.0361

  • CrossRef
  • Google Scholar

• 104

  Rheault F. De Benedictis A. Daducci A. Maffei C. Tax C. M. Romascano D. et al . (2020). Tractostorm: the what, why, and how of tractography dissection reproducibility. Hum. Brain Mapp.41, 1859–1874. doi: 10.1002/hbm.24917

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 105

  Rheault F. Schilling K. G. Obaid S. Begnoche J. P. Cutting L. E. Descoteaux M. et al . (2022a). The influence of regions of interest on tractography virtual dissection protocols: general principles to learn and to follow. Brain Struct. Funct.227, 2191–2207. doi: 10.1007/s00429-022-02518-6

  • CrossRef
  • Google Scholar

• 106

  Rheault F. Schilling K. G. Valcourt-Caron A. Théberge A. Poirier C. Grenier G. et al . (2022b). Tractostorm 2: optimizing tractography dissection reproducibility with segmentation protocol dissemination. Hum. Brain Mapp.43, 2134–2147. doi: 10.1002/hbm.25777

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 107

  Roman C. Guevara M. Valenzuela R. Figueroa M. Houenou J. Duclap D. et al . (2017). Clustering of whole-brain white matter short association bundles using HARDI data. Front. Neuroinform.11:73. doi: 10.3389/fninf.2017.00073

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 108

  Ronneberger O. Fischer P. Brox T . (2015). U-net: convolutional networks for biomedical image segmentation. Medical image computing and computer-assisted intervention–MICCAI 2015. 18th International Conference, Munich, Germany, October 5–9, 2015, Proceedings, Part III 18

  • Google Scholar

• 109

  Sadeghi N. Prastawa M. Fletcher P. T. Wolff J. Gilmore J. H. Gerig G. (2013). Regional characterization of longitudinal DT-MRI to study white matter maturation of the early developing brain. NeuroImage68, 236–247. doi: 10.1016/j.neuroimage.2012.11.040

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 110

  Schilling K. G. Rheault F. Petit L. Hansen C. B. Nath V. Yeh F.-C. et al . (2021). Tractography dissection variability: what happens when 42 groups dissect 14 white matter bundles on the same dataset?NeuroImage243:118502. doi: 10.1016/j.neuroimage.2021.118502

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 111

  Schmitt B. Lebois A. Duclap D. Guevara P. Poupon F. Rivière D. et al . (2012). CONNECT/ARCHI: an open database to infer atlases of the human brain connectivity. ESMRMB272:2012.

  • Google Scholar

• 112

  Sharmin N. Olivetti E. Avesani P. (2018). White matter tract segmentation as multiple linear assignment problems. Front. Neurosci.11:754. doi: 10.3389/fnins.2017.00754

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 113

  Shi J. Malik J. (2000). Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell.22, 888–905. doi: 10.1109/34.868688

  • CrossRef
  • Google Scholar

• 114

  Siless V. Chang K. Fischl B. Yendiki A. (2018). AnatomiCuts: hierarchical clustering of tractography streamlines based on anatomical similarity. NeuroImage166, 32–45. doi: 10.1016/j.neuroimage.2017.10.058

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 115

  Soh S.-E. Tint M. T. Gluckman P. D. Godfrey K. M. Rifkin-Graboi A. Chan Y. H. et al . (2014). Cohort profile: growing up in Singapore towards healthy outcomes (GUSTO) birth cohort study. Int. J. Epidemiol.43, 1401–1409. doi: 10.1093/ije/dyt125

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 116

  Steketee R. M. Bron E. E. Meijboom R. Houston G. C. Klein S. Mutsaerts H. J. et al . (2016). Early-stage differentiation between presenile Alzheimer’s disease and frontotemporal dementia using arterial spin labeling MRI. Eur. Radiol.26, 244–253. doi: 10.1007/s00330-015-3789-x

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 117

  Teeuw J. Caan M. W. Olabarriaga S. D . (2015). Robust automated white matter pathway reconstruction for large studies. Medical image computing and computer-assisted intervention--MICCAI 2015, 18th International Conference, Munich, Germany, October 5–9, 2015, Proceedings, Part I 18

  • Google Scholar

• 118

  Toga A. W. Mazziotta J. C. (2002). Brain mapping: the methods, vol. 1Academic Press.

  • Google Scholar

• 119

  Tunc B. Parker W. A. Ingalhalikar M. Verma R. (2014). Automated tract extraction via atlas based adaptive clustering. NeuroImage102, 596–607. doi: 10.1016/j.neuroimage.2014.08.021

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 120

  Ugurlu D. Firat Z. Ture U. Unal G. (2019). Supervised classification of white matter fibers based on neighborhood fiber orientation distributions using an ensemble of neural networks. Comput. Diffusion MRI2018:22. doi: 10.1007/978-3-030-05831-9_12

  • CrossRef
  • Google Scholar

• 121

  Van Essen D. C. Smith S. M. Barch D. M. Behrens T. E. Yacoub E. Ugurbil K. et al . (2013). The WU-Minn human connectome project: an overview. NeuroImage80, 62–79. doi: 10.1016/j.neuroimage.2013.05.041

  • CrossRef
  • Google Scholar

• 122

  Van Essen D. C. Ugurbil K. Auerbach E. Barch D. Behrens T. E. Bucholz R. et al . (2012). The human connectome project: a data acquisition perspective. NeuroImage62, 2222–2231. doi: 10.1016/j.neuroimage.2012.02.018

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 123

  Vanderweyen D. C. Theaud G. Sidhu J. Rheault F. Sarubbo S. Descoteaux M. et al . (2020). The role of diffusion tractography in refining glial tumor resection. Brain Struct. Funct.225, 1413–1436. doi: 10.1007/s00429-020-02056-z

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 124

  Vaswani A. Shazeer N. Parmar N. Uszkoreit J. Jones L. Gomez A. N. et al . (2017). Attention is all you need. Adv. Neural Inf. Proces. Syst.30

  • Google Scholar

• 125

  Vázquez A. López-López N. Labra N. Figueroa M. Poupon C. Mangin J.-F. et al . (2019). Parallel optimization of fiber bundle segmentation for massive tractography datasets. 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)

  • Google Scholar

• 126

  Vázquez A. López-López N. Sánchez A. Houenou J. Poupon C. Mangin J.-F. et al . (2020). FFClust: fast fiber clustering for large tractography datasets for a detailed study of brain connectivity. NeuroImage220:117070. doi: 10.1016/j.neuroimage.2020.117070

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 127

  Viola P. Jones M . (2001). Rapid object detection using a boosted cascade of simple features. In Proceedings of the 2001 IEEE computer society conference on computer vision and pattern recognition. CVPR 2001

  • Google Scholar

• 128

  Volkow N. D. Koob G. F. Croyle R. T. Bianchi D. W. Gordon J. A. Koroshetz W. J. et al . (2018). The conception of the ABCD study: from substance use to a broad NIH collaboration. Dev. Cogn. Neurosci.32, 4–7. doi: 10.1016/j.dcn.2017.10.002

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 129

  Vu T. H. Monga V. (2017). Fast low-rank shared dictionary learning for image classification. IEEE Trans. Image Process.26, 5160–5175. doi: 10.1109/TIP.2017.2729885

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 130

  Wakana S. Caprihan A. Panzenboeck M. M. Fallon J. H. Perry M. Gollub R. L. et al . (2007). Reproducibility of quantitative tractography methods applied to cerebral white matter. NeuroImage36, 630–644. doi: 10.1016/j.neuroimage.2007.02.049

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 131

  Wang Z. Lv Y. He M. Ge E. Qiang N. Ge B. (2022). Accurate corresponding Fiber tract segmentation via FiberGeoMap learner. International Conference on Medical Image Computing and Computer-Assisted Intervention

  • Google Scholar

• 132

  Warrington S. Bryant K. L. Khrapitchev A. A. Sallet J. Charquero-Ballester M. Douaud G. et al . (2020). XTRACT-standardised protocols for automated tractography in the human and macaque brain. NeuroImage217:116923. doi: 10.1016/j.neuroimage.2020.116923

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 133

  Wassermann D. Makris N. Rathi Y. Shenton M. Kikinis R. Kubicki M. et al . (2013). On describing human white matter anatomy: the white matter query language. Med. Image Comput. Comput. Assist. Interv.16, 647–654. doi: 10.1007/978-3-642-40811-3_81

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 134

  Wassermann D. Makris N. Rathi Y. Shenton M. Kikinis R. Kubicki M. et al . (2016). The white matter query language: a novel approach for describing human white matter anatomy. Brain Struct. Funct.221, 4705–4721. doi: 10.1007/s00429-015-1179-4

  • CrossRef
  • Google Scholar

• 135

  Wasserthal J. Neher P. F. Hirjak D. Maier-Hein K. H. (2019). Combined tract segmentation and orientation mapping for bundle-specific tractography. Med. Image Anal.58:101559. doi: 10.1016/j.media.2019.101559

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 136

  Wasserthal J. Neher P. Maier-Hein K. H. (2018). TractSeg - fast and accurate white matter tract segmentation. NeuroImage183, 239–253. doi: 10.1016/j.neuroimage.2018.07.070

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 137

  Wu Y. Hong Y. Ahmad S. Lin W. Shen D. Yap P. T. et al . (2020). Tract dictionary learning for fast and robust recognition of Fiber bundles. Med. Image Comput. Comput. Assist. Interv.12267, 251–259. doi: 10.1007/978-3-030-59728-3_25

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 138

  Xiao H. Li L. Liu Q. Zhu X. Zhang Q. (2023). Transformers in medical image segmentation: a review. Biomed. Signal Process. Control84:104791. doi: 10.1016/j.bspc.2023.104791

  • CrossRef
  • Google Scholar

• 139

  Xu C. Sun G. Liang R. Xu X. (2021). Vector field streamline clustering framework for brain fiber tract segmentation. IEEE Transac. Cognit. Develop. Syst.14, 1066–1081. doi: 10.1109/TCDS.2021.3094555

  • CrossRef
  • Google Scholar

• 140

  Xu H. Xue T. Liu D. Zhang F. Westin C.-F. Kikinis R. et al . (2023). A registration-and uncertainty-based framework for white matter tract segmentation with only one annotated subject. 2023 IEEE 20th International Symposium on Biomedical Imaging (ISBI)

  • Google Scholar

• 141

  Xue T. Zhang F. Zhang C. Chen Y. Song Y. Golby A. J. et al . (2023). Superficial white matter analysis: an efficient point-cloud-based deep learning framework with supervised contrastive learning for consistent tractography parcellation across populations and dMRI acquisitions. Med. Image Anal.85:102759. doi: 10.1016/j.media.2023.102759

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 142

  Yamada K. Sakai K. Akazawa K. Yuen S. Nishimura T. (2009). MR tractography: a review of its clinical applications. Magn. Reson. Med. Sci.8, 165–174. doi: 10.2463/mrms.8.165

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 143

  Yang Z. Li X. Zhou J. Wu X. Ding Z. (2020). Functional clustering of whole brain white matter fibers. J. Neurosci. Methods335:108626. doi: 10.1016/j.jneumeth.2020.108626

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 144

  Yeatman J. D. Dougherty R. F. Myall N. J. Wandell B. A. Feldman H. M. (2012). Tract profiles of white matter properties: automating fiber-tract quantification. PLoS One7:e49790. doi: 10.1371/journal.pone.0049790

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 145

  Yendiki A. Panneck P. Srinivasan P. Stevens A. Zollei L. Augustinack J. et al . (2011). Automated probabilistic reconstruction of white-matter pathways in health and disease using an atlas of the underlying anatomy. Front. Neuroinform.5:23. doi: 10.3389/fninf.2011.00023

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 146

  Yin H. Xu P. Cui H. Chen G. Ma J. (2022). DC 2 U-net: tract segmentation in brain white matter using dense Criss-cross U-net. International Workshop on Computational Diffusion MRI

  • Google Scholar

• 147

  Yoo S. W. Guevara P. Jeong Y. Yoo K. Shin J. S. Mangin J.-F. et al . (2015). An example-based multi-atlas approach to automatic labeling of white matter tracts. PLoS One10:e0133337. doi: 10.1371/journal.pone.0133337

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 148

  Yun S. Han D. Oh S. J. Chun S. Choe J. Yoo Y. (2019). Cutmix: regularization strategy to train strong classifiers with localizable features. Proceedings of the IEEE/CVF international conference on computer vision

  • Google Scholar

• 149

  Yushkevich P. A. Zhang H. Simon T. J. Gee J. C. (2008). Structure-specific statistical mapping of white matter tracts. NeuroImage41, 448–461. doi: 10.1016/j.neuroimage.2008.01.013

  • CrossRef
  • Google Scholar

• 150

  Zhang H. Cisse M. Dauphin Y. N. Lopez-Paz D. (2017). mixup: Beyond empirical risk minimization. arXivpreprint arXiv:1710.09412

  • Google Scholar

• 151

  Zhang F. Daducci A. He Y. Schiavi S. Seguin C. Smith R. E. et al . (2022). Quantitative mapping of the brain’s structural connectivity using diffusion MRI tractography: a review. NeuroImage249:118870. doi: 10.1016/j.neuroimage.2021.118870

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 152

  Zhang F. Hoffmann N. Karayumak S. C. Rathi Y. Golby A. J. O’Donnell L. J. (2019). Deep white matter analysis: fast, consistent tractography segmentation across populations and dMRI acquisitions. Int. Conference Med. Image Comput. Comput. Assist. Intervent. doi: 10.1007/978-3-030-32248-9_67

  • CrossRef
  • Google Scholar

• 153

  Zhang F. Karayumak S. C. Hoffmann N. Rathi Y. Golby A. J. O’Donnell L. J. (2020). Deep white matter analysis (DeepWMA): fast and consistent tractography segmentation. Med. Image Anal.65:101761. doi: 10.1016/j.media.2020.101761

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 154

  Zhang F. Wells W. M. O’Donnell L. J. (2021). Deep diffusion MRI registration (DDMReg): a deep learning method for diffusion MRI registration. IEEE Trans. Med. Imaging41, 1454–1467. doi: 10.1109/TMI.2021.3139507

  • CrossRef
  • Google Scholar

• 155

  Zhang F. Wu Y. Norton I. Rigolo L. Rathi Y. Makris N. et al . (2018). An anatomically curated fiber clustering white matter atlas for consistent white matter tract parcellation across the lifespan. NeuroImage179, 429–447. doi: 10.1016/j.neuroimage.2018.06.027

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 156

  Zhang Y. Zhang J. Oishi K. Faria A. V. Jiang H. Li X. et al . (2010). Atlas-guided tract reconstruction for automated and comprehensive examination of the white matter anatomy. NeuroImage52, 1289–1301. doi: 10.1016/j.neuroimage.2010.05.049

  • Pubmed Abstract
  • CrossRef
  • Google Scholar

• 157

  Zhao Y. Su J. Yang Z. Ding Z. (2022). A Riemannian framework for functional clustering of whole brain white matter fibers. 2022 IEEE 19th International Symposium on Biomedical Imaging (ISBI)

  • Google Scholar