SegHeD: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints

• URL: http://arxiv.org/abs/2410.01766v1
• Date: Wed, 2 Oct 2024 17:21:43 GMT
• Title: SegHeD: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints
• Authors: Berke Doga Basaran, Xinru Zhang, Paul M. Matthews, Wenjia Bai,
• Abstract summary: Machine learning models have demonstrated a great potential for automated MS lesion segmentation. SegHeD is a novel multi-dataset multi-task segmentation model that can incorporate heterogeneous data as input. SegHeD is assessed on five MS datasets and achieves a high performance in all, new, and vanishing-lesion segmentation.
• Score: 1.498084483844508
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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