Longitudinal Segmentation of MS Lesions via Temporal Difference Weighting
This work addresses the need for accurate monitoring of disease progression in MS patients, representing an incremental improvement over existing longitudinal methods.
The paper tackled the problem of segmenting Multiple Sclerosis lesions in longitudinal MRI scans by introducing a method that explicitly incorporates temporal differences between scans, achieving superior scores in lesion segmentation and detection compared to state-of-the-art models.
Accurate segmentation of Multiple Sclerosis (MS) lesions in longitudinal MRI scans is crucial for monitoring disease progression and treatment efficacy. Although changes across time are taken into account when assessing images in clinical practice, most existing deep learning methods treat scans from different timepoints separately. Among studies utilizing longitudinal images, a simple channel-wise concatenation is the primary albeit suboptimal method employed to integrate timepoints. We introduce a novel approach that explicitly incorporates temporal differences between baseline and follow-up scans through a unique architectural inductive bias called Difference Weighting Block. It merges features from two timepoints, emphasizing changes between scans. We achieve superior scores in lesion segmentation (Dice Score, Hausdorff distance) as well as lesion detection (lesion-level $F_1$ score) as compared to state-of-the-art longitudinal and single timepoint models across two datasets. Our code is made publicly available at www.github.com/MIC-DKFZ/Longitudinal-Difference-Weighting.