AI 中文总结
本研究训练nnU-Net模型，对比不同输入配置与架构，发现仅用DWI训练的基线nnU-Net可快速准确分割急性缺血性脑卒中病灶，性能优于DeepISLES，或可支持临床工作。
AI 中文摘要
目的：扩散加权MRI（DWI-MRI）是可视化和量化急性缺血性脑卒中（AIS）的金标准。尽管深度学习方法可准确分割AIS病灶，但最佳图像输入和模型架构仍不明确。本研究评估采用最小预处理且推理时间符合临床实际的实用深度学习方法，能否实现准确的AIS病灶分割。材料与方法：对自配置的nnU-Net模型进行训练，训练数据来自本地、国家及公开数据集的1744例DWI病例，测试数据为436例病例。采用五折交叉验证评估四种实验条件：是否进行脑提取，以及仅使用DWI或DWI加表观扩散系数（ADC）图像作为输入。比较两种架构：基线nnU-Net（base）和残差编码器nnU-Net（ResEnc）。以2022年ISLES挑战赛的DeepISLES集成模型作为基准进行性能对比。结果：在测试集（n=436）中，base模型的Dice相似系数（DSC）中位数（四分位距）为0.84（0.19）。对于base模型，六种输入配置的两两比较中仅两种存在显著差异。ResEnc在DWI、DWI+脑提取、DWI+ADC输入下的DSC较base模型有小幅但显著提升（均p<0.02），但在DWI+ADC+脑提取输入下无显著差异（p>0.50）。base模型显著优于DeepISLES，尤其在梗死体积较小的患者中（符号秩检验，p<0.01）。结论：仅基于DWI训练、无预处理的基线nnU-Net可实现快速准确的AIS病灶分割，这种简化方法或可推动临床研究并支持急性脑卒中成像工作流程。
英文摘要
Objective: Diffusion-weighted MRI (DWI-MRI) is the gold standard for visualizing and quantifying acute ischaemic stroke (AIS). Although deep learning methods can accurately segment AIS lesions, the optimal image inputs and model architecture remain uncertain. We evaluated whether accurate AIS lesion segmentation can be achieved using a pragmatic deep learning approach with minimal preprocessing and clinically feasible inference times. Materials and Methods: Self-configured nnU-Net models were trained on 1,744 DWI cases from local, national, and open-access datasets and tested on 436 cases. Four experimental conditions were evaluated using five-fold cross-validation: with or without brain extraction and using either DWI alone or DWI plus apparent diffusion coefficient (ADC) images as inputs. Two architectures were compared: the baseline nnU-Net (base) and a residual encoder nnU-Net (ResEnc). Performance was benchmarked against the DeepISLES ensemble model from the 2022 ISLES challenge. Results: In the test set (n=436), the base model achieved a median (IQR) Dice similarity coefficient (DSC) of 0.84 (0.19). For the base model, only two of six pairwise comparisons between input configurations showed significant differences. ResEnc produced small but significant improvements in DSC compared with the base model for DWI, DWI+brain extraction, and DWI+ADC inputs (all p<0.02), but not for DWI+ADC+brain extraction (p>0.50). The base model significantly outperformed DeepISLES, particularly in patients with smaller infarct volumes (signed-rank test, p<0.01). Conclusions: A baseline nnU-Net trained on DWI alone, without preprocessing, enabled fast and accurate AIS lesion segmentation. This streamlined approach may facilitate clinical research and support acute stroke imaging workflows
Comments19 pages, 8 figure and 4 tables
