Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

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S. Denner, A. Khakzar, M. Sajid, M. Saleh, Z. Spiclin, S.T. Kim, N. Navab
Spatio-temporal learning from longitudinal data for multiple sclerosis lesion segmentation
The first two authors contributed equally.
BrainLes? at International Conference on Medical Image Computing and Computer-Assisted Intervention.

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 will be publicly available.</TD>
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