Department of Data Science and Visualization, Faculty of Informatics, University of Debrecen, Debrecen, Hungary.
Department of Pathology, Kenezy Gyula Hospital and Clinic, University of Debrecen, Debrecen, Hungary.
Sci Data. 2024 Jul 6;11(1):733. doi: 10.1038/s41597-024-03566-9.
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.
一种简单而廉价的宫颈癌识别方法是利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，遵循欧洲的建议对阴性涂片进行筛查，以减少假阴性病例。这个过程的第一步是对细胞进行分割。这项任务需要一个大型的、手动分割的数据集，该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，其中包含封闭细胞的准确手动分割。总的来说，APACS23（2023 年用于细胞分割的注释巴氏涂片图像）数据集包含大约 37000 个手动分割的细胞，并分为专门的训练和测试部分，可用于科学研究的官方基准测试或大型挑战赛。
