Event Abstract

K-Surfer: A KNIME-based tool for the management and analysis of human brain MRI FreeSurfer/FSL Data

  • 1 Magna Graecia University of Catanzaro, Department of Medical and Surgical Sciences, Italy
  • 2 University of Reading, School of System Engineering, United Kingdom

Diagnostic imaging techniques such as positron emission tomography (PET), magnetic resonance imaging (MRI), functional MRI (fMRI) and diffusion tensor imaging (DTI), represent nowadays the primary source of information in Neuroscience. A new challenge for neuroscientists is discovering knowledge by merging multi-source and multi-format data from imaging, genomics, proteomics and clinical evidences. As data size and complexity grow, the manual workflow for such analysis becomes time consuming and errors prone. Several software for neuroimaging processing aim to automatize the pre-processing of neuroimages by using a modular approach. FreeSurfer [1] and the FMRIB Software Library (FSL) [2] are popular examples of tools that can be used in conjunction for conducting both volume-based and surface-based analysis of human brain MRI. However extracting the multi-dimensional data (volume, thickness, diffusion indices) generated by FreeSurfer and FSL is not straightforward and requires an accurate and detailed knowledge of their tools, conventions and file formats. Furthermore, statistical analysis and data mining have to be performed by using external analytics platforms and the importing of neurological data into such tools, represents a crucial phase. Among several commercial and open-source software for data analytics, the Konstanz Information Miner (KNIME) [3,4], has received high satisfaction ratings in the last edition of the largest survey of data mining, data science and data analytics professionals in the industry [5]. This work presents K-Surfer , a novel and unique KNIME plug-in for brain MRI data. K-Surfer facilitates the design and deployment of fully automated workflows for extracting, managing and mining FreeSurfer and FSL data. It also integrates the FreeSurfer tool for the visualisation of 3D brain tracts into KNIME to allow an immediate comparison of numerical and visual findings. K-Surfer consists of five new KNIME modules (nodes), available in the Node Repository of KNIME (Fig. 1.a), with specific functionalities: (i) the node FSDDIoverall (FreeSurfer Diffusion Data Import overall) extracts anisotropy and diffusivity values averaged over an entire pathway; (ii) the node FSDDIbyvoxel (FreeSurfer Diffusion Data Import by voxel) extracts several measures as a function of the position along the trajectory of the pathway; (iii) the node FSVDI (FreeSurfer Volume Data Import) extracts the volumes of specific structures, as determined by the subcortical segmentation; (iv) the node FSTDI (FreeSurfer Thickness Data Import) extracts several measures, including the thickness of specific structures, as determined by the cortical segmentation; (v) the node FSPV (FreeSurfer Pathways Viewer visualises the probability distribution of single white-matter pathways or all white-matter pathways simultaneously. K-Surfer also includes two meta nodes that extends the previous functionalities: (i) the node Add Class Attribute (overall) can be used for adding a new column containing the class attribute to a diffusion data table; (ii) the node Select multiple tracts (overall) can be used for extracting the diffusion values of more than one tract at once. A sample KNIME workflow demonstrating the K-Surfer nodes and meta nodes is depicted in Fig. 1.b and in the Fig.1.c the Node Description for the selected node is visualised. The nodes have user-friendly configuration dialogs (see Fig. 2 for an example) and do not require the user to write UNIX shell commands and scripts as required when using FreeSurfer and FSL directly. The main goal of K-Surfer is to extend KNIME so to provide a specific environment for the study of neurological data, reducing time costs and human errors. Furthermore, K-Surfer extends some current functionalities of FreeSurfer scripts for extracting data, adding new features such as importing measures related to more brain tracts, selecting subjects from different studies, and merging demographic, genomics and proteomics data.
The KNIME extension K-Surfer is freely available at https://sourceforge.net/projects/ksurfer/ for non-commercial use.

Figure 1
Figure 2


The Author Alessia Sarica is funded by the European Commission, European Social Fund and “Regione Calabria”. Authors had full control of the design of the study, methods used, outcome parameters and results, analysis of data and production of the manuscript.


[1] Dale AM, Fischl B, Sereno MI. Cortical surface-based analysis. I. segmentation and surface reconstruction. NeuroImage 1999;9:17994.
[2] S.M. Smith, M. Jenkinson, M.W. Woolrich, C.F. Beckmann, T.E.J. Behrens, H. Johansen-Berg, P.R. Bannister, M. De Luca, I. Drobnjak, D.E. Flitney, R. Niazy, J. Saunders, J. Vickers, Y. Zhang, N. De Stefano, J.M. Brady, and P.M. Matthews. Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage, 23(S1):208-219, 2004
[3] M. Berthold, N. Cebron, F. Dill, G. Di Fatta, T. Gabriel, F. Georg, T. Meinl, P. Ohl, C. Sieb, B. Wiswedel, "KNIME: the Konstanz Information Miner", Proceedings of the Workshop on Multi-Agent Systems and Simulation (MAS&S), 4th Annual Industrial Simulation Conference (ISC), Palermo, Italy, June 5-7, 2006, pp.58-61.
[4] Michael R. Berthold, Nicolas Cebron, Fabian Dill, Thomas R. Gabriel, Tobias Kotter, Thorsten Meinl, Peter Ohl, Kilian Thiel, and Bernd Wiswedel, 'KNIME - the Konstanz Information Miner: Version 2.0 and Beyond', SIGKDD Explor. Newsl., 11 (2009), 26-31.
[5] Rexer, Karl. Rexer Analytics 2013 Data Miner Survey, 2013. [Online: http://www.rexeranalytics.com/Data-Miner-Survey-2013-Intro.html].

Keywords: MRI, DTI, FreeSurfer, fsl, Data Workflows, Data Mining, KNIME

Conference: Neuroinformatics 2014, Leiden, Netherlands, 25 Aug - 27 Aug, 2014.

Presentation Type: Demo, to be considered for oral presentation

Topic: General neuroinformatics

Citation: Sarica A, Di Fatta G and Cannataro M (2014). K-Surfer: A KNIME-based tool for the management and analysis of human brain MRI FreeSurfer/FSL Data. Front. Neuroinform. Conference Abstract: Neuroinformatics 2014. doi: 10.3389/conf.fninf.2014.18.00003

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Received: 01 Apr 2014; Published Online: 04 Jun 2014.

* Correspondence: Miss. Alessia Sarica, Magna Graecia University of Catanzaro, Department of Medical and Surgical Sciences, Catanzaro, Italy, sarica@unicz.it