NVIDIA has collaborated with 12 global institutions to create a comprehensive pancreatic CT dataset, with the aim of improving early-detection rates for pancreatic cancer.
The Pancreatic Tumor Segmentation dataset (PanTS) includes 36,000 scans from 145 institutions, with expert-validated annotations for over 993,000 structures, including tumors, regions of the pancreas, and 24 surrounding anatomical structures (e.g., vascular and skeletal structures, abdominal and thoracic organs).
The PanTS dataset uses NVIDIA’s open-source medical imaging AI framework, MONAI, for pancreatic tumor detection, localization, and segmentation. In analysis, AI models trained on PanTS have performed significantly better than models trained on existing public datasets, findings the developers say are attributable to the PanTS dataset’s 16x larger-scale tumor annotations, supported by the inclusion of the 24 additional surrounding anatomical structures.
Pancreatic cancer is the third-leading cause of U.S. cancer deaths; 80% to 85% of cases are detected too late for effective treatment.
Read about PanTS and get access to the dataset here.
![Axial images from unenhanced calcium score cardiac CT (left) and curved planar reformation images from CT angiography (right) show that higher long-term exposure to air pollution is associated with greater coronary artery calcium and more obstructive coronary artery disease (CAD). Top row: Images in a 68-year-old male patient with higher 10-year mean ambient air pollution exposure (7.9 μg/m3 for particulate matter measuring ≤2.5 μm in diameter [PM2.5] and 17.4 parts per billion [ppb] for NO2) with extensive CAD (coronary artery calcium score [CACS] >1,000 and obstructive CAD [≥70% diameter stenosis]). Bottom row: Images in a 57-year-old female patient with lower 10-year mean ambient air pollution exposure (6.3 μg/m3 for PM2.5 and 4.6 ppb for NO2) with no CAD (CACS = 0 and no obstructive stenosis).](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/06/hanneman.r6SMLzkezo.png?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
