An Exceptional Dataset For Rare Pancreatic Tumor Segmentation

TL;DR

This paper introduces the first dedicated pNETs dataset with 469 annotated CT scans, and proposes a new slice-wise weight loss function to improve segmentation accuracy of these rare tumors.

Contribution

The creation of a specialized pNETs dataset and a novel loss function for UNet models to enhance segmentation performance.

Findings

The dataset contains 469 patient scans with detailed annotations.

The new loss function improves segmentation accuracy.

Baseline models show promising results on the dataset.

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.