超大规模胰腺肿瘤影像数据集，助力AI精准识别与分割。
PanTS: The Pancreatic Tumor Segmentation Dataset
- 整合145家机构3.6万例CT扫描，标注99万+体素级结构
- 模型在肿瘤检测与分割上性能显著优于现有公开数据集
- 适合医学影像AI研究者、放射科医生及胰腺癌辅助诊断开发
PanTS是一个大规模、多中心的胰腺CT分析数据集，包含来自145家医疗机构的36,390例CT扫描，提供超过993,000个体素级别的专家验证标注，涵盖胰腺肿瘤、胰头、体、尾部以及24个周围解剖结构（如血管、骨骼及腹胸腔器官）。每例扫描均附带患者年龄、性别、诊断、对比剂期相、层间距、层厚等元数据。在PanTS上训练的AI模型，在胰腺肿瘤检测、定位和分割任务中表现显著优于基于现有公开数据集训练的模型。分析表明，性能提升直接源于16倍更大的肿瘤标注规模，间接得益于24个额外解剖结构的支持。作为目前最大最全面的同类资源，PanTS为胰腺CT分析中AI模型的研发与评估提供了新基准。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。