@inproceedings{3c24a26b405240d3b0f637b79a726458,
title = "Spatio-Temporal Learning from Longitudinal Data for Multiple Sclerosis Lesion Segmentation",
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 ).",
keywords = "Longitudinal analysis, MS lesion segmentation",
author = "Stefan Denner and Ashkan Khakzar and Moiz Sajid and Mahdi Saleh and Ziga Spiclin and Kim, \{Seong Tae\} and Nassir Navab",
note = "Publisher Copyright: {\textcopyright} 2021, Springer Nature Switzerland AG.; 6th International MICCAI Brainlesion Workshop, BrainLes 2020 Held in Conjunction with 23rd Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2020 ; Conference date: 04-10-2020 Through 04-10-2020",
year = "2021",
doi = "10.1007/978-3-030-72084-1\_11",
language = "English",
isbn = "9783030720834",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "111--121",
editor = "Alessandro Crimi and Spyridon Bakas",
booktitle = "Brainlesion",
}