标题： PanTS：胰腺肿瘤分割数据集 显示英文标题

标题： PanTS: The Pancreatic Tumor Segmentation Dataset

摘要： PanTS是一个大规模、多机构的数据集，旨在推进胰腺CT分析的研究。 它包含来自145家医疗中心的36,390个CT扫描，具有专家验证的体素级注释，超过993,000个解剖结构，涵盖胰腺肿瘤、胰头、胰体和胰尾，以及24个周围的解剖结构，如血管/骨骼结构和腹部/胸部器官。 每个扫描都包括患者年龄、性别、诊断、对比阶段、层内间距、切片厚度等元数据。 在PanTS上训练的AI模型在胰腺肿瘤检测、定位和分割方面的性能明显优于在现有公开数据集上训练的模型。 我们的分析表明，这些提升直接归因于16倍更大的肿瘤注释规模，间接得益于24个额外的周围解剖结构。 作为此类资源中最大和最全面的资源，PanTS为开发和评估胰腺CT分析中的AI模型提供了新的基准。 显示英文摘要

摘要： PanTS is a large-scale, multi-institutional dataset curated to advance research in pancreatic CT analysis. It contains 36,390 CT scans from 145 medical centers, with expert-validated, voxel-wise annotations of over 993,000 anatomical structures, covering pancreatic tumors, pancreas head, body, and tail, and 24 surrounding anatomical structures such as vascular/skeletal structures and abdominal/thoracic organs. Each scan includes metadata such as patient age, sex, diagnosis, contrast phase, in-plane spacing, slice thickness, etc. AI models trained on PanTS achieve significantly better performance in pancreatic tumor detection, localization, and segmentation compared to those trained on existing public datasets. Our analysis indicates that these gains are directly attributable to the 16x larger-scale tumor annotations and indirectly supported by the 24 additional surrounding anatomical structures. As the largest and most comprehensive resource of its kind, PanTS offers a new benchmark for developing and evaluating AI models in pancreatic CT analysis.