SegHeD: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints
<|reference_start|>SegHeD: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints: Assessment of lesions and their longitudinal progression from brain magnetic resonance (MR) images plays a crucial role in diagnosing and monitoring multiple sclerosis (MS). Machine learning models have demonstrated a great potential for automated MS lesion segmentation. Training such models typically requires large-scale high-quality datasets that are consistently annotated. However, MS imaging datasets are often small, segregated across multiple sites, with different formats (cross-sectional or longitudinal), and diverse annotation styles. This poses a significant challenge to train a unified MS lesion segmentation model. To tackle this challenge, we present SegHeD, a novel multi-dataset multi-task segmentation model that can incorporate heterogeneous data as input and perform all-lesion, new-lesion, as well as vanishing-lesion segmentation. Furthermore, we account for domain knowledge about MS lesions, incorporating longitudinal, spatial, and volumetric constraints into the segmentation model. SegHeD is assessed on five MS datasets and achieves a high performance in all, new, and vanishing-lesion segmentation, outperforming several state-of-the-art methods in this field.<|reference_end|>
@article{basaran2024seghed:,
    title={SegHeD: Segmentation of Heterogeneous Data for Multiple Sclerosis
  Lesions with Anatomical Constraints},
    author={Berke Doga Basaran, Xinru Zhang, Paul M. Matthews, Wenjia Bai},
    journal={arXiv preprint arXiv:2410.01766},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.01766},
    primaryClass={eess.IV cs.CV cs.LG}
}