Login
- Dokumenttyp:
- Zeitschriftenaufsatz
- Autor(en):
- Denner, S.; Khakzar, A.; Sajid, M.; Saleh, M.; Spiclin, Z.; Kim, S.T.; Navab, N.
- Titel:
- 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 will be publicly available.
- Stichworte:
- Longitudinal Analysis,MS Lesion Segmentation
- Zeitschriftentitel:
- BrainLes at International Conference on Medical Image Computing and Computer-Assisted Intervention
- Jahr:
- 2020
BibTeX