ISLA: A U-Net for MRI-based acute ischemic stroke lesion segmentation with deep supervision, attention, domain adaptation, and ensemble learning

TL;DR

This paper introduces ISLA, a deep learning model based on U-Net with attention, deep supervision, and domain adaptation, achieving improved accuracy in MRI-based stroke lesion segmentation across multiple datasets.

Contribution

The paper presents a new U-Net based model, ISLA, with optimized architecture and training strategies for better stroke lesion segmentation and generalization across datasets.

Findings

ISLA outperforms existing methods on external test data.

Systematic optimization improves segmentation robustness.

Unsupervised domain adaptation enhances external dataset performance.

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.