Conference paper · Book chapter
Simultaneous Whole-Brain Segmentation and White Matter Lesion Detection Using Contrast-Adaptive Probabilistic Models
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.
Language: | English |
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Publisher: | Springer |
Year: | 2016 |
Pages: | 9-20 |
Proceedings: | 1st International Workshop on Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries (Brainles 2015) |
Series: | Lecture Notes in Computer Science |
Journal subtitle: | Revised Selected Papers |
ISBN: | 3319308572 , 3319308580 , 9783319308579 and 9783319308586 |
ISSN: | 03029743 |
Types: | Conference paper and Book chapter |
DOI: | 10.1007/978-3-319-30858-6_2 |
ORCIDs: | Puonti, Oula and Van Leemput, Koen |