Accurate delineation of acute ischemic stroke lesions in MRI is a key component of stroke diagnosis and management. In recent years, deep learning models have been successfully applied to the automatic segmentation of such lesions. While most proposed architectures are based on the U-Net framework, they primarily differ in their choice of loss functions and in the use of deep supervision, residual connections, and attention mechanisms. Moreover, many implementations are not publicly available, and the optimal configuration for acute ischemic stroke (AIS) lesion segmentation remains unclear. In this work, we introduce ISLA (Ischemic Stroke Lesion Analyzer), a new deep learning model for AIS lesion segmentation from diffusion MRI, trained on three multicenter databases totaling more than 1500 AIS participants. Through systematic optimization of the loss function, convolutional architecture, deep supervision, and attention mechanisms, we developed a robust segmentation framework. We further investigated unsupervised domain adaptation to improve generalization to an external clinical dataset. ISLA outperformed two state-of-the-art approaches for AIS lesion segmentation on an external test set. Codes and trained models will be made publicly available to facilitate reuse and reproducibility.

翻译：在磁共振成像中精确勾画急性缺血性卒中病灶是卒中诊断与治疗的关键环节。近年来，深度学习模型已成功应用于此类病灶的自动分割。尽管现有架构大多基于U-Net框架，其主要差异体现在损失函数的选择以及深度监督、残差连接和注意力机制的应用上。此外，许多实现方案尚未公开，且针对急性缺血性卒中病灶分割的最优配置仍不明确。本研究提出ISLA（缺血性卒中病灶分析器），这是一种基于弥散磁共振成像的急性缺血性卒中病灶分割新模型，该模型在总计超过1500名急性缺血性卒中参与者的三个多中心数据库上进行训练。通过对损失函数、卷积架构、深度监督及注意力机制的系统优化，我们开发出鲁棒的分割框架。为进一步提升模型在外部临床数据集上的泛化能力，我们探索了无监督域适应方法。在外部测试集上，ISLA的表现优于两种当前最先进的急性缺血性卒中病灶分割方法。代码与训练模型将公开发布，以促进复用性与可重现性研究。