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 and reducing the workload of doctors. However, the absence of publicly available image datasets restricts the application of computer-assisted diagnostic techniques. In this paper, a publicly available (ECPC-IDS) are published. Specifically, the segmentation section includes PET and CT images, with 7159 images in multiple formats totally. In order to prove the effectiveness of segmentation on ECPC-IDS, six deep learning semantic segmentation methods are selected to test the image segmentation task. The object detection section also includes PET and CT images, with 3579 images and XML files with annotation information totally. Eight deep learning methods are selected for experiments on the detection task. This study is conduct using deep learning-based semantic segmentation and object detection methods to demonstrate the distinguishability on ECPC-IDS. From a separate perspective, the minimum and maximum values of Dice on PET images are 0.546 and 0.743, respectively. The minimum and maximum values of Dice on CT images are 0.012 and 0.510, respectively. The target detection section’s maximum mAP values on PET and CT images are 0.993 and 0.986, respectively. As far as we know, this is the first publicly available dataset of endometrial cancer with a large number of multi-modality images. ECPC-IDS can assist researchers in exploring new algorithms to enhance computer-assisted diagnosis, benefiting both clinical doctors and patients. ECPC-IDS is also freely published for non-commercial at: .

中文翻译：

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ECPC-IDS：用于评估语义分割和检测高代谢区域的基准子宫内膜癌 PET/CT 图像数据集

子宫内膜癌是女性生殖系统最常见的肿瘤之一，是仅次于卵巢癌和宫颈癌的第三大常见妇科恶性肿瘤。早期诊断可以显着提高患者的5年生存率。随着人工智能的发展，计算机辅助诊断在提高诊断的准确性和客观性、减轻医生的工作量方面发挥着越来越重要的作用。然而，缺乏公开可用的图像数据集限制了计算机辅助诊断技术的应用。在本文中，发布了一个公开可用的（ECPC-IDS）。具体来说，分割部分包括PET和CT图像，总共7159张多种格式的图像。为了证明ECPC-IDS上分割的有效性，选择了6种深度学习语义分割方法来测试图像分割任务。物体检测部分还包括PET和CT图像，总共3579张图像和带有注释信息的XML文件。选择八种深度学习方法进行检测任务的实验。本研究使用基于深度学习的语义分割和对象检测方法来证明 ECPC-IDS 的可区分性。从单独的角度来看，PET图像上Dice的最小值和最大值分别为0.546和0.743。 CT 图像上 Dice 的最小值和最大值分别为 0.012 和 0.510。目标检测部分在PET和CT图像上的最大mAP值分别为0.993和0.986。据我们所知，这是第一个公开的子宫内膜癌数据集，包含大量多模态图像。 ECPC-IDS可以协助研究人员探索新的算法来增强计算机辅助诊断，使临床医生和患者受益。 ECPC-IDS 还免费发布用于非商业用途： 。