发表机构
University of Bern; Bern University Hospital; St. Marianna University School of Medicine; University Hospital Zurich; Guangdong Provincial People’s Hospital（伯尔尼大学; 伯尔尼大学医院; 圣玛丽安娜医科大学; 苏黎世大学医院; 广东省人民医院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出统一框架用于心脏CT综合分割与表型分析，结合人在回路标注、增强技术与自监督预训练模型，组建大型数据集，在多数据集上表现优于开源工具，提高标注效率，推动心脏表型分析发展，还提供了可重复基础。
AI 中文摘要
从计算机断层扫描（CT）对心脏结构进行全面量化，目前受限的并非数据可用性，而是测量的可扩展性，这使得常规应用不切实际。本文提出了一个用于心脏CT综合分割与表型分析的统一框架，它结合了人在回路标注流程、心脏CT增强技术以及在60000张未标注心脏CT扫描上预训练的自监督基础模型。利用此方法，组装了迄今为止最大且最全面的专家标注心脏CT分割数据集，包含1598个病例和14种不同心脏结构。在五个外部数据集上，该框架比现有开源工具能更准确、全面地分割所有结构。自监督预训练提高了标注效率，在低数据情况下外部评估时收益显著。跨卷积、变压器和状态空间架构的基准测试显示性能相当，表明数据质量和预训练而非架构推动了准确性。该框架扩展到了人群水平的表型分析，分割的解剖结构携带了有关心室功能和疾病严重程度的功能相关信息。通过公开发布带有人工标注的最大数据集、代码、模型权重、CT增强库和软件，这项工作为从常规获取的CT扫描中进行机会性心脏表型分析提供了可重复的基础。
英文摘要
Comprehensive quantification of cardiac structures from computed tomography (CT) remains limited not by data availability but by the scalability of measurements, which makes routine use impractical. Here we present a unified framework for comprehensive cardiac CT segmentation and phenotyping that combines a human-in-the-loop annotation pipeline, a cardiac CT augmentation technique, and a self-supervised foundation model pre-trained on 60,000 unlabeled cardiac CT scans. Using this approach, we assembled the largest and most comprehensive expert-annotated cardiac CT segmentation dataset to date, comprising 1598 cases and 14 distinct cardiac structures (1000 for training, 598 for the external test set). Across five external datasets, the framework segmented all structures more accurately and comprehensively than existing open-source tools. Self-supervised pre-training improved labeling efficiency, with the most significant gains observed during external evaluation in the low-data regime. Benchmarking across convolutional, transformer, and state-space architectures showed comparable performance, indicating that data quality and pre-training, rather than architecture, drove accuracy. The framework was scaled to population-level phenotyping, with segmented anatomy that carries functionally relevant information about ventricular function and disease severity beyond demographic variables. By openly releasing the largest dataset with human labels, code, model weights, a CT augmentation library, and software, this work provides a reproducible foundation for opportunistic cardiac phenotyping from routinely acquired CT scans.
