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Endometrial cancer is one of the most common tumors in the femalereproductive system and is the third most common gynecological malignancy thatcauses death after ovarian and cervical cancer. Early diagnosis cansignificantly improve the 5-year survival rate of patients. With thedevelopment of artificial intelligence, computer-assisted diagnosis plays anincreasingly important role in improving the accuracy and objectivity ofdiagnosis, as well as reducing the workload of doctors. However, the absence ofpublicly available endometrial cancer image datasets restricts the applicationof computer-assisted diagnostic techniques.In this paper, a publicly availableEndometrial Cancer PET/CT Image Dataset for Evaluation of Semantic Segmentationand Detection of Hypermetabolic Regions (ECPC-IDS) are published. Specifically,the segmentation section includes PET and CT images, with a total of 7159images in multiple formats. In order to prove the effectiveness of segmentationmethods on ECPC-IDS, five classical deep learning semantic segmentation methodsare selected to test the image segmentation task. The object detection sectionalso includes PET and CT images, with a total of 3579 images and XML files withannotation information. Six deep learning methods are selected for experimentson the detection task.This study conduct extensive experiments using deeplearning-based semantic segmentation and object detection methods todemonstrate the differences between various methods on ECPC-IDS. As far as weknow, this is the first publicly available dataset of endometrial cancer with alarge number of multiple images, including a large amount of informationrequired for image and target detection. ECPC-IDS can aid researchers inexploring new algorithms to enhance computer-assisted technology, benefitingboth clinical doctors and patients greatly.

ECPC-IDS:A benchmark endometrail cancer PET/CT image dataset for evaluation of semantic segmentation and detection of hypermetabolic regions