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Patch-Based Segmentation from MP2RAGE Images: Comparison to Conventional Techniques: Patch-Based Techniques in Medical Imaging

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In structural and functional MRI studies there is a need for robust and accurate automatic segmentation of various brain structures. We present a comparison study of three automatic segmentation methods based on the new T1-weighted MR sequence called MP2RAGE, which has superior soft tissue contrast. Automatic segmentations of the thalamus and hippocampus are compared to manual segmentations. In addition, we qualitatively evaluate the segmentations when warped to co-registered maps of the fractional anisotropy (FA) of water diffusion. Compared to manual segmentation, the best results were obtained with a patch-based segmentation method (volBrain) using a library of images from the same scanner (local), followed by volBrain using an external library (external), FSL and Freesurfer. The qualitative evaluation showed that volBrain local and volBrain external produced almost no segmentation errors when overlaid on FA maps, while both FSL and Freesurfer segmentations were found to overlap with white matter tracts. These results underline the importance of applying accurate and robust segmentation methods and demonstrate the superiority of patch-based methods over more conventional methods.
Original languageEnglish
Title of host publicationPatch-Based Techniques in Medical Imaging
EditorsGuorong Wu, Pierrick Coupé, Yiqiang Zhan, Brent Munsell, Daniel Rueckert
Number of pages8
Volume9467
PublisherSpringer International Publishing
Publication year2015
Pages180-187
ISBN (print)978-3-319-28193-3
DOIs
Publication statusPublished - 2015

    Research areas

  • Patch-based segmentation, MRI, volBrain, Freesurfer, FSL, MP2RAGE

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