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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Acknowledgments
This work was supported, in part, by the NIH grants NIBIB R01EB017255, NINDS R01NS082347, NINDS R01NS085211, NINDS R21NS093349, NINDS R01NS094456.
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Dong, M., Oguz, I., Subbana, N., Calabresi, P., Shinohara, R.T., Yushkevich, P. (2017). Multiple Sclerosis Lesion Segmentation Using Joint Label Fusion. In: Wu, G., Munsell, B., Zhan, Y., Bai, W., Sanroma, G., Coupé, P. (eds) Patch-Based Techniques in Medical Imaging. Patch-MI 2017. Lecture Notes in Computer Science(), vol 10530. Springer, Cham. https://doi.org/10.1007/978-3-319-67434-6_16
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DOI: https://doi.org/10.1007/978-3-319-67434-6_16
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