Chapter title |
Multiple Sclerosis Lesion Segmentation Using Joint Label Fusion
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Chapter number | 16 |
Book title |
Patch-Based Techniques in Medical Imaging
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Published in |
Patch-based techniques in medical imaging : third International Workshop, Patch-MI 2017, held in conjunction with MICCAI 2017, Quebec City, QC, Canada, September 14, 2017, Proceedings. Patch-MI (Workshop) (3rd : 2017 : Quebec, Quebec), September 2017
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DOI | 10.1007/978-3-319-67434-6_16 |
Pubmed ID | |
Book ISBNs |
978-3-31-967433-9, 978-3-31-967434-6
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Authors |
Mengjin Dong, Ipek Oguz, Nagesh Subbana, Peter Calabresi, Russell T. Shinohara, Paul Yushkevich |
Abstract |
This paper adapts the joint label fusion (JLF) multi-atlas image segmentation algorithm to the problem of multiple sclerosis (MS) lesion segmentation in multi-modal MRI. Conventionally, JLF requires a set of atlas images to be co-registered to the target image using deformable registration. However, given the variable spatial distribution of lesions in the brain, whole-brain deformable registration is unlikely to line up lesions between atlases and the target image. As a solution, we propose to first pre-segment the target image using an intensity regression based technique, yielding a set of "candidate" lesions. Each "candidate" lesion is then matched to a set of similar lesions in the atlas based on location and size; and deformable registration and JLF are applied at the level of the "candidate" lesion. The approach is evaluated on a dataset of 74 subjects with MS and shown to improve Dice similarity coefficient with reference manual segmentation by 12% over intensity regression technique. |
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