TY - GEN
T1 - Multiple Sclerosis Lesion Segmentation Using Joint Label Fusion
AU - Dong, Mengjin
AU - Oguz, Ipek
AU - Subbana, Nagesh
AU - Calabresi, Peter
AU - Shinohara, Russell T.
AU - Yushkevich, Paul
N1 - Funding Information:
Acknowledgments. This work was supported, in part, by the NIH grants NIBIB R01EB017255, NINDS R01NS082347, NINDS R01NS085211, NINDS R21NS093349, NINDS R01NS094456.
Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - 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.
AB - 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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U2 - 10.1007/978-3-319-67434-6_16
DO - 10.1007/978-3-319-67434-6_16
M3 - Conference contribution
C2 - 29707700
AN - SCOPUS:85029410629
SN - 9783319674339
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 138
EP - 145
BT - Patch-Based Techniques in Medical Imaging - 3rd International Workshop, Patch-MI 2017 Held in Conjunction with MICCAI 2017, Proceedings
A2 - Zhan, Yiqiang
A2 - Bai, Wenjia
A2 - Wu, Guorong
A2 - Coupe, Pierrick
A2 - Munsell, Brent C.
A2 - Sanroma, Gerard
PB - Springer Verlag
T2 - 3rd International Workshop on Patch-Based Techniques in Medical Imaging, Patch-MI 2017 held in conjunction with 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017
Y2 - 14 September 2017 through 14 September 2017
ER -