缺血性卒中分割及多中心非增强CT上净水摄取量化的监督目标域自适应

中文摘要 AI 辅助

目的：非增强计算机断层扫描（NCCT）上梗死低密度的定量评估，包括净水摄取（NWU），需要手动或半手动病灶勾画，通常需借助CT灌注或弥散加权MRI引导，限制了临床适用性。NCCT上的自动分割可实现NWU等生物标志物的高效提取，但在异质性多中心数据中仍具挑战。本研究旨在开发并外部测试一种面向缺血性卒中NCCT分割的域感知深度学习框架，并评估其用于NWU量化的适用性。材料与方法：在这项来自四个数据集的801例患者的回顾性多中心研究中，基于nnU-Net的模型在汉堡-埃彭多夫大学医学中心和急性缺血性卒中数据集中的NCCT扫描上训练。为适应新域，模型在来自波士顿（n=11）和ISLES（n=75）的目标域子集上微调，并在未用于微调的保留病例上评估。自动分割和NWU值与专家参考进行比较。结果：对于≥30 mL的病灶，中位Dice为0.68（波士顿）和0.56（ISLES）。纳入在ISLES中占主导的较小病灶后，急性病灶分割（波士顿数据集）的中位Dice为0.54（四分位距[IQR] 0.30-0.70），而NCCT病灶分割与治疗后梗死（ISLES挑战的主要目标）相比时中位Dice为0.20（IQR 0.03-0.41）。自动NWU平均绝对误差为1.37个百分点（标准差1.61，波士顿）。结论：目标域自适应支持了跨异质性外部队列的仅NCCT梗死分割，尽管性能在不同域间存在差异。该方法实现了无需高级影像即可从基线NCCT进行低误差NWU量化，支持进一步的前瞻性临床评估。

英文摘要

Objectives: Quantitative assessment of infarct hypodensity on non-contrast computed tomography (NCCT), including net water uptake (NWU), requires manual or semi-manual lesion delineation, often guided by CT perfusion or diffusion-weighted MRI, limiting clinical applicability. Automated segmentation on NCCT could enable efficient biomarker extraction such as NWU but remains challenging across heterogeneous multicenter data. This study aimed to develop and externally test a domain-aware deep learning framework for ischemic stroke segmentation on NCCT and assess its suitability for NWU quantification. Materials & Methods: In this retrospective multicenter study of 801 patients from four datasets, an nnU-Net-based model was trained on NCCT scans from the University Medical Center Hamburg-Eppendorf and the Acute Ischemic Stroke Dataset. To adapt to new domains, the model was fine-tuned on target-domain subsets from Boston (n=11) and ISLES (n=75), with evaluation on held-out cases not used for fine-tuning. Automated segmentations and NWU values were compared with expert references. Results: For lesions $\geq$ 30 mL, median Dice was 0.68 (Boston) and 0.56 (ISLES). Including smaller lesions, which predominated in ISLES, median Dice was 0.54 (interquartile range [IQR] 0.30-0.70) for acute lesion segmentation (Boston dataset) and 0.20 (IQR 0.03-0.41) for NCCT lesion segmentations when compared to post-treatment infarct (primary target of the ISLES challenge). Automated NWU mean absolute error was 1.37 percentage points (SD 1.61, Boston). Conclusion: Target-domain adaptation supported NCCT-only infarct segmentation across heterogeneous external cohorts, although performance varied across domains. The approach enabled low-error NWU quantification from baseline NCCT without advanced imaging, supporting further prospective clinical evaluation.

发表机构

• University Medical Center Hamburg-Eppendorf（汉堡-埃彭多夫大学医学中心）
• University Hospital Marburg（马尔堡大学医院）
• Massachusetts General Hospital（麻省总医院）
• Harvard Medical School（哈佛医学院）
• HELIOS Medical Center Schwerin（HELIOS什未林医疗中心）
• MSH Medical School Hamburg（汉堡MSH医学院）
• MSH Research, Development and Innovation GmbH（MSH研究开发与创新有限公司）
• MSH Medical University of Applied Sciences and Medical University（MSH应用科学医科大学与医科大学）

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