Abstract
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.
Abstract (translated)
在MRI中准确地描绘急性缺血性脑卒中的病变是诊断和管理的重要组成部分。近年来，深度学习模型已被成功应用于此类病变的自动分割任务上。尽管大多数提出的架构基于U-Net框架，但它们主要通过选择不同的损失函数、深层监督、残差连接以及注意力机制来区别开来。此外，许多实现并未公开发布，并且针对急性缺血性脑卒中（AIS）病变分割的最佳配置仍然不明确。 在这项工作中，我们引入了ISLA（缺血性脑卒中病变分析仪），这是一种新的深度学习模型，用于从弥散加权MRI图像中进行AIS病变的分割。该模型是在三个多中心数据库上训练而成的，这些数据库包含超过1500名急性缺血性脑卒中的参与者的数据。通过系统地优化损失函数、卷积架构、深层监督以及注意力机制，我们开发了一个稳健的分割框架。此外，为了提高在外部临床数据集上的泛化能力，我们还研究了无监督领域自适应技术。 ISLA在外部分割测试集中超越了两个最先进的AIS病变分割方法。代码和训练好的模型将被公开发布，以促进再利用和可重复性研究。
URL
https://arxiv.org/abs/2601.08732
