Spatio-Temporal Learning From Longitudinal Data for Multiple Sclerosis Lesion Segmentation

MCML Authors

Ashkan Khakzar

Dr.

* Former Member

→ Group Daniel Rückert
 Artificial Intelligence in Healthcare and Medicine

Nassir Navab

Prof. Dr.

Core PI

Computer Aided Medical Procedures & Augmented Reality

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).

inproceedings DKS+20

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BrainLes @MICCAI 2020

Workshop on Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries at the 23rd International Conference on Medical Image Computing and Computer Assisted Intervention. Virtual, Oct 04-08, 2020.

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Authors

S. Denner • A. Khakzar • M. Sajid • M. Saleh • Z. Spiclin • S. T. Kim • N. Navab

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Links

DOI GitHub

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Research Area

 C1 | Medicine