PanTS: The Pancreatic Tumor Segmentation Dataset
Journal:
arXiv
Published Date:
Jul 2, 2025
Abstract
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.
