首个专用于胰腺神经内分泌瘤的标注CT数据集，助力罕见肿瘤精准分割。
An Exceptional Dataset For Rare Pancreatic Tumor Segmentation
- 构建469例患者专用的增强CT数据集，聚焦胰腺神经内分泌瘤
- 提出切片加权损失函数，提升UNet模型分割性能
- 填补领域空白，适合医学影像与肿瘤诊断研究者使用
胰腺神经内分泌瘤（pNETs）是极为罕见的内分泌肿瘤，占所有胰腺恶性肿瘤的5%以下，发病率仅为1-1.5例/10万。早期检测对提高患者生存率至关重要，但其罕见性使从CT图像中分割pNETs极具挑战。目前尚无专门针对pNETs的研究数据集。为此，本文提出首个专注于pNETs的高质量标注增强型计算机断层扫描（CECT）数据集，涵盖469名患者数据，是首个仅包含pNETs的公开数据集。此外，我们还为基于UNet的模型设计了一种新的切片级加权损失函数，显著提升了分割性能。本数据集旨在推动pNETs的医学理解与诊断，促进更精准诊断工具的研发，最终改善患者预后并推动肿瘤学发展。
原文摘要 · Abstract (English)
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.
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