ISLA模型提升MRI卒中病灶分割精度，支持多中心数据泛化。
ISLA: A U-Net for MRI-based acute ischemic stroke lesion segmentation with deep supervision, attention, domain adaptation, and ensemble learning
- 基于U-Net改进，融合注意力与深度监督机制
- 在超1500例数据上训练，外部测试集表现优于现有方法
- 引入无监督域适应，适合临床部署与科研复现
急性缺血性卒中（AIS）的MRI病灶精准分割是诊断与管理的关键。近年来，深度学习模型已成功应用于自动分割。尽管多数架构基于U-Net，但其差异主要体现在损失函数、深度监督、残差连接和注意力机制的选择上。此外，许多实现未公开，最优配置仍不明确。本文提出ISLA（Ischemic Stroke Lesion Analyzer），一种基于扩散MRI的AIS病灶分割新模型，训练于三个多中心数据库，涵盖超过1500名患者。通过系统优化损失函数、卷积结构、深度监督及注意力机制，构建了稳健的分割框架。进一步研究了无监督域适应以提升对外部临床数据集的泛化能力。在外部测试集上，ISLA性能优于两种先进方法。代码与训练模型将公开，促进可重复使用。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。