An Exceptional Dataset For Rare Pancreatic Tumor Segmentation
Journal:
arXiv
Published Date:
Jan 29, 2025
Abstract
Pancreatic NEuroendocrine Tumors (pNETs) are very rare endocrine neoplasms
that account for less than 5% of all pancreatic malignancies, with an incidence
of only 1-1.5 cases per 100,000. Early detection of pNETs is critical for
improving patient survival, but the rarity of pNETs makes segmenting them from
CT a very challenging problem. So far, there has not been a dataset
specifically for pNETs available to researchers. To address this issue, we
propose a pNETs dataset, a well-annotated Contrast-Enhanced Computed Tomography
(CECT) dataset focused exclusively on Pancreatic Neuroendocrine Tumors,
containing data from 469 patients. This is the first dataset solely dedicated
to pNETs, distinguishing it from previous collections. Additionally, we provide
the baseline detection networks with a new slice-wise weight loss function
designed for the UNet-based model, improving the overall pNET segmentation
performance. We hope that our dataset can enhance the understanding and
diagnosis of pNET Tumors within the medical community, facilitate the
development of more accurate diagnostic tools, and ultimately improve patient
outcomes and advance the field of oncology.
