TimeLesSeg: AI Model for MS Lesion Segmentation Across Scenarios
Researchers have introduced TimeLesSeg, a unified contrast-agnostic framework for segmenting multiple sclerosis (MS) lesions using a single convolutional neural network. The model handles both cross-sectional and longitudinal inputs by modeling pathological priors through lesion masks. When no prior information is available, an empty mask enables cross-sectional processing. This approach addresses challenges posed by MS heterogeneity and scanner variability. The work is detailed in arXiv:2605.07955.
Key facts
- TimeLesSeg is a unified contrast-agnostic framework for MS lesion segmentation.
- It uses a single convolutional neural network for both cross-sectional and longitudinal inputs.
- Pathological priors are modeled through lesion masks processed with the current scan.
- Cross-sectional processing is enabled by training with empty masks when no prior is available.
- The model addresses challenges from MS heterogeneity and scanner variability.
- The research is published on arXiv with ID 2605.07955.
- The framework aims to overcome the current divide between cross-sectional and longitudinal approaches.
- The approach is designed to be robust to changes in input structure and distribution.
Entities
Institutions
- arXiv