Abstract
AbstractA 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.
Funder
New National Excellence Program of the Ministry for Culture and Innovation of Hungary
European Union, European Social Fund
Publisher
Springer Science and Business Media LLC
Reference31 articles.
1. Sung, H. et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: A Cancer Journal for Clinicians 71, 209–249, https://doi.org/10.3322/caac.21660 (2021).
2. van der Graaf, Y. & Vooijs, G. P. False negative rate in cervical cytology. Journal of Clinical Pathology 40, 438–442, https://doi.org/10.1136/jcp.40.4.438 (1987).
3. Arbyn, M. et al. (eds.) European Guidelines for Quality Assurance in Cervical Cancer Screening (2 edn, Publications Office of the European Union, Luxembourg, 2008).
4. Hologic, Inc. ThinPrep Imaging System - Operation summary and clinical information. https://www.hologic.com/sites/default/files/package-insert/MAN-03938-001_002_02.pdf (2024).
5. Becton, Dickinson and Company. FocalPoint GS Imaging System. https://www.bd.com/en-us/products-and-solutions/products/product-families/bd-focalpoint-gs-imaging-system (2024).