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Publication Type : Conference Paper
Thematic Areas : Wireless Network and Application
Publisher : Patch Based Techniques in Medical Imaging, MICCAI 2017
Source : Patch Based Techniques in Medical Imaging, MICCAI 2017
Campus : Amritapuri
School : School of Engineering
Center : Amrita Center for Wireless Networks and Applications (AmritaWNA)
Department : Wireless Networks and Applications (AWNA)
Year : 2017
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.
Cite this Research Publication : M. Dong, I. Oguz, N. K. Subbanna, P. Calabresi, R. T. Shinohara, and P. Yuskhevich, “Multiple Sclerosis Lesion Segmentation Using Joint Label Fusion”, Patch Based Techniques in Medical Imaging, MICCAI 2017.