Endometrial cancer is one of the most common tumors in the female reproductive system and is the third most common gynecological malignancy that causes death after ovarian and cervical cancer. Early diagnosis can significantly improve the 5-year survival rate of patients. With the development of artificial intelligence, computer-assisted diagnosis plays an increasingly important role in improving the accuracy and objectivity of diagnosis, as well as reducing the workload of doctors. However, the absence of publicly available endometrial cancer image datasets restricts the application of computer-assisted diagnostic techniques.In this paper, a publicly available Endometrial Cancer PET/CT Image Dataset for Evaluation of Semantic Segmentation and Detection of Hypermetabolic Regions (ECPC-IDS) are published. Specifically, the segmentation section includes PET and CT images, with a total of 7159 images in multiple formats. In order to prove the effectiveness of segmentation methods on ECPC-IDS, five classical deep learning semantic segmentation methods are selected to test the image segmentation task. The object detection section also includes PET and CT images, with a total of 3579 images and XML files with annotation information. Six deep learning methods are selected for experiments on the detection task.This study conduct extensive experiments using deep learning-based semantic segmentation and object detection methods to demonstrate the differences between various methods on ECPC-IDS. As far as we know, this is the first publicly available dataset of endometrial cancer with a large number of multiple images, including a large amount of information required for image and target detection. ECPC-IDS can aid researchers in exploring new algorithms to enhance computer-assisted technology, benefiting both clinical doctors and patients greatly.

翻译：子宫内膜癌是女性生殖系统最常见的肿瘤之一，也是继卵巢癌和宫颈癌之后导致死亡的第三大妇科恶性肿瘤。早期诊断可显著提高患者五年生存率。随着人工智能的发展，计算机辅助诊断在提升诊断准确性与客观性、减轻医生工作负担方面发挥着日益重要的作用。然而，公开可用的子宫内膜癌图像数据集的缺失限制了计算机辅助诊断技术的应用。本文发布了一个公开可用的子宫内膜癌PET/CT图像数据集（ECPC-IDS），用于评估语义分割与高代谢区域检测任务。具体而言，分割部分包含PET和CT图像，共计7159张多格式图像。为验证分割方法在ECPC-IDS上的有效性，选取了五种经典深度学习语义分割方法进行图像分割任务测试。目标检测部分同样包含PET和CT图像，共计3579张图像及带有标注信息的XML文件，并选取六种深度学习方法开展检测任务实验。本研究基于深度学习的语义分割与目标检测方法进行了广泛实验，以展示不同方法在ECPC-IDS上的性能差异。据我们所知，这是首个公开的大规模多模态子宫内膜癌数据集，包含图像与目标检测所需的大量信息。ECPC-IDS可帮助研究人员探索新算法以增强计算机辅助技术，使临床医生和患者均能受益。