ArticleScientific reports2024
An improved approach for automated cervical cell segmentation with PointRend.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of artificial intelligence in cervical cytology: a systematic review of deep learning models, datasets, and reported metrics.Frontiers in big data · 2025Pooled it
- Accurate Segmentation of Overlapping Cervical Cells Using an Optimized Deep Learning Framework for Cytology Screening.Diagnostics (Basel, Switzerland) · 2026Article
- Investigation of cervical cell image segmentation technology based on deep learning and non-coding RNAs.Non-coding RNA research · 2026Article
- Progress in the application research of cervical cancer screening developed by artificial intelligence in large populations.Discover oncology · 2025Review
- Optimized segmentation of overlapping cervical cells based on Mask2Former and denoising.Scientific reports · 2025Article
- Attention-Enhanced Lightweight Architecture with Hybrid Loss for Colposcopic Image Segmentation.Cancers · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Regular screening for cervical cancer is one of the best tools to reduce cancer incidence. Automated cell segmentation in screening is an essential task because it can present better understanding of the characteristics of cervical cells. The main challenge of cell cytoplasm segmentation is that many boundaries in cell clumps are extremely difficult to be identified. This paper proposes a new convolutional neural network based on Mask RCNN and PointRend module, to segment overlapping cervical cells. The PointRend head concatenates fine grained features and coarse features extracted from different feature maps to fine-tune the candidate boundary pixels of cell cytoplasm, which are crucial for precise cell segmentation. The proposed model achieves a 0.97 DSC (Dice Similarity Coefficient), 0.96 TPRp (Pixelwise True Positive Rate), 0.007 FPRp (Pixelwise False Positive Rate) and 0.006 FNRo (Object False Negative Rate) on dataset from ISBI2014. Specially, the proposed method outperforms state-of-the-art result by about
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.