AsymFeX：一种用于跨成像模态和卒中阶段的缺血性卒中分割的对称性驱动框架
AsymFeX: A Symmetry-Driven Framework for Ischemic Stroke Segmentation Across Imaging Modalities and Stroke Stages
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中文总结 AI 辅助
该研究提出与nnU-Net兼容的两阶段3D分割方法AsymFeX，通过对称性驱动设计跨模态跨卒中阶段实现缺血性卒中分割，在AISD等数据集上性能优于现有方法，具备临床应用潜力。
中文摘要 AI 辅助
快速准确地分割急性缺血性卒中（AIS）病灶对卒中预后和治疗规划至关重要。非对比CT（NCCT）是诊断缺血性梗死的一线成像模态，其梗死对比度较微弱，导致手动勾画缓慢且费力。受此启发，同时也基于临床中通过对比大脑半球定位梗死的实践，我们提出了一种与nnU-Net兼容的两阶段3D分割方法。第一阶段校正头部倾斜，将每一次扫描对齐到其真实的解剖正中矢状面；第二阶段应用新型的非对称特征提取（Asymmetric Feature Extraction，AsymFeX）模块，通过跨半球注意力、特征差异估计和双尺度门控，将每个体素与其局部3×3×3邻域内的真实对侧对应体素进行对比，以同时捕获大梗死和小梗死。在AISD数据集上，我们的方法取得了0.6796的Dice系数、23.53 mm的HD95和7.69 mL的AVD，显著优于现有的最先进方法，且在70 mL的溶栓资格阈值下具备临床相关的体积分析能力。在ATLAS v2.1和ISLES'24上的概念验证评估表明，相同的对称性驱动设计无需改变架构即可跨成像模态和卒中时间点泛化，不确定性分析进一步支持了其在临床部署下的可靠性。代码可公开获取。
英文摘要
Fast and accurate segmentation of Acute Ischemic Stroke (AIS) lesions is essential for stroke prognosis and treatment planning. Non-contrast CT (NCCT), the first-line imaging modality for diagnosing ischemic infarcts, exhibits subtle infarct contrast, making manual delineation slow and labor-intensive. Motivated by this, and by the clinical practice of comparing brain hemispheres to localize infarcts, we propose a two-stage, nnU-Net-compatible 3D segmentation method. The first stage corrects head tilt to align each scan to its true anatomical mid-sagittal plane; the second applies a novel Asymmetric Feature Extraction (AsymFeX) module, comparing each voxel to its true contralateral counterpart within a local 3 x 3 x 3 neighborhood via cross-hemispheric attention, feature disparity estimation, and dual-scale gating to capture both large and small infarcts. On AISD, our method achieves 0.6796 Dice, 23.53 mm HD95, and 7.69 mL AVD, significantly outperforming existing state-of-the-art methods, with clinically relevant volumetric analysis at the 70 mL thrombolysis-eligibility threshold. Proof-of-concept evaluation on ATLAS v2.1 and ISLES'24 demonstrates that the same symmetry-driven design generalizes across imaging modalities and stroke time points without architectural changes, further supported by an uncertainty analysis assessing reliability under clinical deployment. Code is publicly available at https://github.com/biomedia-lab/AIS-detection.
发表机构
- Indian Institute of Science（印度科学学院）
机构由 AI 辅助整理，请以论文原文为准。
