Spatio-temporal Learning from Longitudinal Data for Multiple Sclerosis Lesion Segmentation

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.