用简单模型仅靠DWI图像即可快速准确分割急性中风病灶
Deep Learning Segmentation of Diffusion-Weighted MRI Acute Ischaemic Stroke: A Pragmatic Evaluation Across Three Datasets
- 用nnU-Net模型直接输入DWI图，不需复杂预处理
- 在436例测试中平均分割精度达Dice=0.84（IQR=0.19）
- 适合临床研究与急诊影像流程，尤其小病灶更优
目的：扩散加权MRI（DWI-MRI）是可视化和量化急性缺血性中风（AIS）的金标准。尽管深度学习可精准分割病灶，但最优图像输入与模型架构仍不明确。本研究评估了无需复杂预处理、具备临床可行推理时间的实用型深度学习方法能否实现高精度分割。方法：在本地、国家及公开数据集共1744例DWI病例上训练自配置nnU-Net模型，并在436例独立测试集上评估。五折交叉验证下比较四种条件：有无脑组织提取，以及仅使用DWI或联合表观扩散系数（ADC）图像作为输入。对比基线nnU-Net（base）与残差编码器nnU-Net（ResEnc）。性能以2022年ISLES挑战赛的DeepISLES集成模型为基准。结果：测试集中，base模型中位数Dice相似系数（DSC）为0.84（四分位距IQR=0.19）。在六组输入配置对比中，仅两组差异显著。ResEnc在仅用DWI、DWI+脑提取、或DWI+ADC时均带来小幅但显著的提升（全部p<0.02），但在DWI+ADC+脑提取组合中未见显著差异（p>0.50）。base模型显著优于DeepISLES，尤其在小病灶患者中（符号秩检验，p<0.01）。结论：仅使用原始DWI图像、无需预处理的基线nnU-Net即可实现快速且高精度的AIS病灶分割。该简化流程有望推动临床研究并支持急性中风影像工作流。
原文摘要 · Abstract (English)
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
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