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

Denner S, Khakzar A, Sajid M, Saleh M, Spiclin Z, Kim ST, Navab N (2021)

Publication Type: Conference contribution

Publication year: 2021

Journal

Lecture Notes in Computer Science Springer Verlag

Publisher: Springer Science and Business Media Deutschland GmbH

Book Volume: 12658 LNCS

Pages Range: 111-121

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Virtual, Online

ISBN: 9783030720834

DOI: 10.1007/978-3-030-72084-1_11

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 (https://github.com/StefanDenn3r/Spatio-temporal-MS-Lesion-Segmentation ).

Involved external institutions

Technische Universität München (TUM) DE Germany (DE) University of Ljubljana (UL) / Univerza v Ljubljani SI Slovenia (SI)

How to cite

APA:

Denner, S., Khakzar, A., Sajid, M., Saleh, M., Spiclin, Z., Kim, S.T., & Navab, N. (2021). Spatio-Temporal Learning from Longitudinal Data for Multiple Sclerosis Lesion Segmentation. In Alessandro Crimi, Spyridon Bakas (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 111-121). Virtual, Online: Springer Science and Business Media Deutschland GmbH.

MLA:

Denner, Stefan, et al. "Spatio-Temporal Learning from Longitudinal Data for Multiple Sclerosis Lesion Segmentation." Proceedings of the 6th International MICCAI Brainlesion Workshop, BrainLes 2020 Held in Conjunction with 23rd Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2020, Virtual, Online Ed. Alessandro Crimi, Spyridon Bakas, Springer Science and Business Media Deutschland GmbH, 2021. 111-121.

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