识别宫颈癌的一种简单而廉价的方法是使用巴氏涂片图像的光学显微镜分析。在这个领域训练基于人工智能的系统成为可能，例如，遵循欧洲的建议来筛查阴性涂片以减少假阴性病例。这一过程的第一步是对细胞进行分割。此任务需要大型且手动分段的数据集，可用于训练基于深度学习的解决方案。我们描述了一个相应的数据集，其中包含对所包含的单元格进行准确的手动分割。总之，APACS23（Annotated PAp smear images for Cell Segmentation 2023）数据集包含约 37,000 个手动分割的细胞，并分为专用训练和测试部分，可用于科学研究或重大挑战的官方基准。© 2024 . 作者。

A simple and cheap way to recognize cervical cancer is using light microscopic analysis of Pap smear images. Training artificial intelligence-based systems becomes possible in this domain, e.g., to follow the European recommendation to screen negative smears to reduce false negative cases. The first step for such a process is segmenting the cells. A large and manually segmented dataset is required for this task, which can be used to train deep learning-based solutions. We describe a corresponding dataset with accurate manual segmentations for the enclosed cells. Altogether, the APACS23 (Annotated PAp smear images for Cell Segmentation 2023) dataset contains about 37 000 manually segmented cells and is separated into dedicated training and test parts, which could be used for an official benchmark of scientific investigations or a grand challenge.© 2024. The Author(s).