Abstract
In this paper we propose a new generative model for simultaneous brain parcellation and white matter lesion segmentation from multi-contrast magnetic resonance images. The method combines an existing whole-brain segmentation technique with a novel spatial lesion model based on a convolutional restricted Boltzmann machine. Unlike current state-of-the-art lesion detection techniques based on discriminative modeling, the proposed method is not tuned to one specific scanner or imaging protocol, and simultaneously segments dozens of neuroanatomical structures. Experiments on a public benchmark dataset in multiple sclerosis indicate that the method’s lesion segmentation accuracy compares well to that of the current state-of-the-art in the field, while additionally providing robust whole-brain segmentations.
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Notes
- 1.
Manual segmentations from UNC are now also available, but at the time of the challenge this was not the case [10] so we decided to use only the segmentations provided by CHB.
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Acknowledgements
This research was supported by NIH NCRR (P41-RR14075), NIBIB (R01EB013565), the Lundbeck Foundation (R141-2013-13117), and financial contributions from the Technical University of Denmark.
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Puonti, O., Van Leemput, K. (2016). Simultaneous Whole-Brain Segmentation and White Matter Lesion Detection Using Contrast-Adaptive Probabilistic Models. In: Crimi, A., Menze, B., Maier, O., Reyes, M., Handels, H. (eds) Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2015. Lecture Notes in Computer Science(), vol 9556. Springer, Cham. https://doi.org/10.1007/978-3-319-30858-6_2
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