Cx22: A new publicly available dataset for deep learning-based segmentation of cervical cytology images. (November 2022)

• Record Type: Journal Article
• Authors: Liu, Guangqi; Ding, Qinghai; Luo, Haibo; Sha, Min; Li, Xiang; Ju, Moran
• Is Part Of: Computers in biology and medicine. Volume 150(2022)
• Publication Date: 2022-11
• Subjects: Cervical cell dataset -- Image segmentation -- Instance segmentation -- Semantic segmentation -- Deep learning; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2022.106194
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: The segmentation of cervical cytology images plays an important role in the automatic analysis of cervical cytology screening. Although deep learning-based segmentation methods are well-developed in other image segmentation areas, their application in the segmentation of cervical cytology images is still in the early stage. The most important reason for the slow progress is the lack of publicly available and high-quality datasets, and the study on the deep learning-based segmentation methods may be hampered by the present datasets which are either artificial or plagued by the issue of false-negative objects. In this paper, we develop a new dataset of cervical cytology images named Cx22, which consists of the completely annotated labels of the cellular instances based on the open-source images released by our institute previously. Firstly, we meticulously delineate the contours of 14, 946 cellular instances in1320 images that are generated by our proposed ROI-based label cropping algorithm. Then, we propose the baseline methods for the deep learning-based semantic and instance segmentation tasks based on Cx22. Finally, through the experiments, we validate the task suitability of Cx22, and the results reveal the impact of false-negative objects on the performance of the baseline methods. Based on our work, Cx22 can provide a foundation for fellow researchers to develop high-performance deep learning-based methods for the segmentation of cervical cytology images. Other detailed information and step-by-step guidance on accessing the dataset are made available to fellow researchers at https://github.com/LGQ330/Cx22 . Graphical abstract: Image 1 Highlights: Cx22 is the first publicly available dataset suitable for deep learning-based cervical cell segmentation. Cx22 contains 1320 authentic images with 14, 946 well-annotated cellular instance labels. Cx22 is suitable for the multi-task of cervical cytology images, including semantic and instance segmentation and detection-related tasks. The ROI-based label cropping algorithm has low algorithmic complexity in shape reconstruction based on two-dimensional annotation points.