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arXiv · 2004.03675

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

Stefan Denner·Ashkan Khakzar·Moiz Sajid·Mahdi Saleh·Ziga Spiclin·Seong Tae Kim·Nassir Navab
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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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Stefan Denner, Ashkan Khakzar, Moiz Sajid, Mahdi Saleh, Ziga Spiclin, Seong Tae Kim, Nassir Navab. 2020-04-07. Spatio-temporal Learning from Longitudinal Data for Multiple Sclerosis Lesion Segmentation. https://arxiv.org/abs/2004.03675

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