Spatio-temporal Learning from Longitudinal Data for Multiple Sclerosis Lesion Segmentation
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Segmentation of Multiple Sclerosis (MS) lesions in longitudinal brain MR scans is performed for monitoring the progression of MS lesions. In order to improve segmentation, we use spatio-temporal cues in longitudinal data. To that end, we propose two approaches: Our longitudinal segmentation architecture which is grounded upon early-fusion of longitudinal data. And complementary to the longitudinal architecture, we propose a novel multi-task learning approach by defining an auxiliary self-supervised task of deformable registration between two time-points to guide the neural network toward learning from spatio-temporal changes. We show the effectiveness of our methods on two datasets: An in-house dataset comprised of 70 patients with one follow-up study for each patient and the ISBI longitudinal MS lesion segmentation challenge dataset which has 19 patients with three to five follow-up studies. Our results show that spatio-temporal information in longitudinal data is a beneficial cue for improving segmentation. Code is publicly available.
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