Cx22: a new publicly available dataset for deep learning-based segmentation of cervical cytology images
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.
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End-to-end convolutional neural networks have dominated performance at diverse medical imaging tasks.
“Motivated by the dominant performance of CNNs, it is desirable to develop an end-to-end network for automatic placental maturity grading (PMG) from ultrasound images.”
From DilatedFormer: dilated granularity transformer network for placental maturity grading in ultrasound (2023) · cites this paper as [14]
Deep learning algorithms have shown remarkable success in medical image analysis due to their ability to learn complex features from limited data.
“Deep learning algorithms, on the other hand, have shown remarkable success in these tasks due to their ability to learn complex features from limited data [12 -14].”
From DilatedFormer: dilated granularity transformer network for placental maturity grading in ultrasound (2023) · cites this paper as [14]
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DilatedFormer: dilated granularity transformer network for placental maturity grading in ultrasound
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- Authors
- Liu, G., Ding, Q., Luo, H., Sha, M., Li, X., & Ju, M.
- Affiliations
- Shenyang Institute of Automation (CN)
- Northeastern University (CN)
- Dalian Maritime University (CN)
- Corresponding author
- Luo, Haibo
- Venue
- Computers in Biology and Medicine
- Publisher
- Elsevier BV
- Published
- 2022-11
- Volume
- 150
- Pages
- 106194
- ISSN
- 0010-4825 (ISSN-L)
- OpenAlex
- W4306167223
- Cited by
- 81
- References
- 34
- Citations by year
- 2026: 16 · 2025: 16 · 2024: 21 · 2023: 27
- FWCI
- 5.26
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Computers in Biology and Medicine (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Keywords
- cervical cell segmentation, instance segmentation, semantic segmentation, cervical cancer screening
- MeSH terms
- Deep Learning, Algorithms, Image Processing, Computer-Assisted, Semantics
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