Automatic multiple sclerosis (MS) lesion segmentation using multi-contrast
magnetic resonance (MR) images provides improved efficiency and reproducibility
compared to manual delineation. Current state-of-the-art automatic MS lesion
segmentation methods utilize modified U-Net-like architectures. However, in the
literature, dedicated architecture modifications were always required to
maximize their performance. In addition, the best-performing methods have not
proven to be generalizable to diverse test datasets with contrast variations
and image artifacts. In this work, we developed an accurate and generalizable
MS lesion segmentation model using the well-known U-Net architecture without
further modification. A novel test-time self-ensembled lesion fusion strategy
is proposed that not only achieved the best performance using the ISBI 2015 MS
segmentation challenge data but also demonstrated robustness across various
self-ensemble parameter choices. Moreover, equipped with instance normalization
rather than batch normalization widely used in literature, the model trained on
the ISBI challenge data generalized well on clinical test datasets from
different scanners.

利用 U-Net 架构开发了准确且具有泛化能力的多发性硬化症损伤分割模型，并提供了一种新颖的测试时自组合损伤融合策略，不需要进一步的修改，同时具备实例归一化，而非文献中广泛使用的批归一化，在不同扫描仪的临床测试数据上展示了良好的泛化能力。