ORIGINAL RESEARCH article
Differences between MR brain region segmentation methods: impact on single-subject analysis
- 1Department of Radiology and Nuclear Medicine, Erasmus Medical Center, Netherlands
- 2Department of Epidemiology, Erasmus Medical Center, Netherlands
- 3Philips Research (Germany), Germany
- 4School of Biomedical Engineering and Imaging Sciences, King’s College London, United Kingdom
- 5Department of Computing, Faculty of Engineering, Imperial College London, United Kingdom
- 6Faculty of Applied Sciences, Delft University of Technology, Netherlands
For the segmentation of magnetic resonance brain images into anatomical regions, numerous fully automated methods have been proposed and compared to reference segmentations obtained manually. However, systematic differences might exist between the resulting segmentations, dependent on the segmentation method and underlying brain atlas. This
potentially results in sensitivity differences to disease and can further complicate the comparison of individual patients to normative data. In this study, we aim to answer two research questions: 1) to what extent are methods interchangeable, as long as the same method is being used for computing normative volume distributions and patient-specific volumes? and 2) can different methods be used for computing normative volume distributions and assessing patient-specific volumes? To answer these questions, we compared volumes of six brain regions calculated by five state-of-the-art segmentation methods: Erasmus MC (EMC), FreeSurfer (FS), geodesic information flows (GIF), multi-atlas label propagation with expectation-maximisation (MALP-EM), and model-based brain segmentation (MBS). We applied the methods on 988 non-demented (ND) subjects and computed the correlation (PCC-v) and absolute agreement (ICC-v) on the volumes. For most regions the PCC-v was good (>0,75) indicating that volume differences between methods in ND subjects are mainly due to systematic differences. The ICC-v was generally lower, especially for the smaller regions, indicating that it is essential that the same method is used to generate normative and patient data. To evaluate the impact on single-subject analysis we also applied the methods to 42 patients with Alzheimer’s disease (AD). In the case where the normative distributions and the patient-specific volumes were calculated by the same method, the patient’s distance to the normative distribution was assessed with the z-score. We determined the diagnostic value of this z-score, which showed to be consistent across methods. The absolute agreement on the AD patients’ z-scores was high for regions thalamus and putamen. This is encouraging as it indicates that the studied methods are interchangeable for these regions. For regions hippocampus, amygdala, caudate nucleus and accumbens, and globus pallidus, not all method combinations showed a high ICC-z. Whether two methods are indeed interchangeable should be confirmed for the specific application and dataset of interest.
Keywords: Brain region segmentation, sub-cortical, comparison study, Normative modeling, Magnetic Resonance Imaging
Received: 28 Jun 2020;
Accepted: 21 May 2021.
Copyright: © 2021 Huizinga, Poot, Vinke, Wenzel, Bron, Tousaint, Ledig, Vrooman, Ikram, Niessen, Vernooij and Klein. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Dr. Wyke Huizinga, Department of Radiology and Nuclear Medicine, Erasmus Medical Center, Rotterdam, Netherlands, firstname.lastname@example.org