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

• URL: http://arxiv.org/abs/2004.03675v2
• Date: Sat, 26 Sep 2020 19:42:07 GMT
• Title: Spatio-temporal Learning from Longitudinal Data for Multiple Sclerosis Lesion Segmentation
• Authors: Stefan Denner, Ashkan Khakzar, Moiz Sajid, Mahdi Saleh, Ziga Spiclin, Seong Tae Kim, Nassir Navab
• Abstract summary: We show efficacy of our method on a clinical dataset comprised of 70 patients. We improve the result of current state-of-the-art by 2.6% in terms of overall score.
• Score: 42.25397672438179
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Segmentation of Multiple Sclerosis (MS) lesions in longitudinal brain MR scans is performed for monitoring the progression of MS lesions. We hypothesize that the spatio-temporal cues in longitudinal data can aid the segmentation algorithm. Therefore, we propose a 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 efficacy of our method on a clinical dataset comprised of 70 patients with one follow-up study for each patient. Our results show that spatio-temporal information in longitudinal data is a beneficial cue for improving segmentation. We improve the result of current state-of-the-art by 2.6% in terms of overall score (p<0.05). Code is publicly available.

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