TY - GEN
T1 - Longitudinal patch-based segmentation of multiple sclerosis white matter lesions
AU - Roy, Snehashis
AU - Carass, Aaron
AU - Prince, Jerry L.
AU - Pham, Dzung L.
N1 - Funding Information:
S. Roy—Support for this work included funding from the Department of Defense in the Center for Neuroscience and Regenerative Medicine and by the grants NIH/NINDS R01NS070906.
Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - Segmenting T2-weighted white matter lesions from longitudinal MR images is essential in understanding progression of multiple sclerosis. Most lesion segmentation techniques find lesions independently at each time point, even though there are different noise and image contrast variations at each point in the time series. In this paper, we present a patch based 4D lesion segmentation method that takes advantage of the temporal component of longitudinal data. For each subject with multiple time-points, 4D patches are constructed from the T1-w and FLAIR scans of all time-points. For every 4D patch from a subject, a few relevant matching 4D patches are found from a reference, such that their convex combination reconstructs the subject’s 4D patch. Then corresponding manual segmentation patches of the reference are combined in a similar manner to generate a 4D membership of lesions of the subject patch. We compare our 4D patch-based segmentation with independent 3D voxel-based and patch-based lesion segmentation algorithms. Based on ground truth segmentations from 30 data sets, we show that the mean Dice coefficients between manual and automated segmentations improve after using the 4D approach compared to two state-of-the-art 3D segmentation algorithms.
AB - Segmenting T2-weighted white matter lesions from longitudinal MR images is essential in understanding progression of multiple sclerosis. Most lesion segmentation techniques find lesions independently at each time point, even though there are different noise and image contrast variations at each point in the time series. In this paper, we present a patch based 4D lesion segmentation method that takes advantage of the temporal component of longitudinal data. For each subject with multiple time-points, 4D patches are constructed from the T1-w and FLAIR scans of all time-points. For every 4D patch from a subject, a few relevant matching 4D patches are found from a reference, such that their convex combination reconstructs the subject’s 4D patch. Then corresponding manual segmentation patches of the reference are combined in a similar manner to generate a 4D membership of lesions of the subject patch. We compare our 4D patch-based segmentation with independent 3D voxel-based and patch-based lesion segmentation algorithms. Based on ground truth segmentations from 30 data sets, we show that the mean Dice coefficients between manual and automated segmentations improve after using the 4D approach compared to two state-of-the-art 3D segmentation algorithms.
UR - http://www.scopus.com/inward/record.url?scp=84952059686&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84952059686&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-24888-2_24
DO - 10.1007/978-3-319-24888-2_24
M3 - Conference contribution
C2 - 27570846
AN - SCOPUS:84952059686
SN - 9783319248875
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 194
EP - 202
BT - Machine Learning in Medical Imaging - 6th International Workshop, MLMI 2015 Held in Conjunction with MICCAI 2015, Proceedings
A2 - Zhou, Luping
A2 - Shi, Yinghuan
A2 - Wang, Li
A2 - Wang, Qian
PB - Springer Verlag
T2 - 6th International Workshop on Machine Learning in Medical Imaging, MLMI 2015 and Held in Conjunction with 18th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2015
Y2 - 5 October 2015 through 5 October 2015
ER -