多源脑影像融合，精准分割多发性硬化病灶变化
SegHeD: Segmentation of Heterogeneous Data for Multiple Sclerosis Lesions with Anatomical Constraints
- 融合跨站点异构数据，支持全病灶/新病灶/消失病灶分割
- 引入时空体积约束，提升病灶演变一致性与准确性
- 适配小样本、多格式数据，适合临床纵向研究场景
多发性硬化（MS）的诊断与监测依赖于脑部磁共振（MR）图像中病灶的评估及其随时间的变化。机器学习模型在自动分割病灶方面展现出巨大潜力，但训练通常需要大规模、高质量且一致标注的数据集。然而，现有的MS影像数据集往往规模小、分散于多个机构，格式各异（横断面或纵向），标注风格不一，难以训练统一的分割模型。为此，我们提出SegHeD——一种新型多数据集多任务分割模型，可直接处理异构数据输入，实现全病灶、新病灶及消失病灶的联合分割。同时，模型融入了关于MS病灶的领域知识，引入纵向、空间与体积约束机制。在五个独立的MS数据集上评估，SegHeD在所有类型病灶分割任务中均表现优异，优于当前多项先进方法。
原文摘要 · Abstract (English)
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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