ArticleDiagnostic pathology2022
Application of the sliding window method and Mask-RCNN method to nuclear recognition in oral cytology.
Article in Diagnostic pathology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Deep learning single-cell analysis for cytologic evaluation of oral potentially malignant disorders.Scientific reports · 2026Article
- Systematic evaluation of computational methods for cell segmentation.Briefings in bioinformatics · 2026Article
- Enhanced temporal encoding-decoding for survival analysis of multimodal clinical data in smart healthcare.Visual computing for industry, biomedicine, and art · 2025Article
- Establishing reference values for normal tongue squamous cells: An investigation of atypical changes.CytoJournal · 2025Article
- The urine formed element instance segmentation based on YOLOv5n.Scientific reports · 2024Article
- Automatic segmentation of ameloblastoma on ct images using deep learning with limited data.BMC oral health · 2024Article
- AI performance in oral cytology for differentiating poorly defined tumor cells from reactive atypia.Journal of oral biology and craniofacial researchArticle
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Authors and funding
10 authors.
Funding
Abstract
backgroundWe aimed to develop an artificial intelligence (AI)-assisted oral cytology method, similar to cervical cytology. We focused on the detection of cell nuclei because the ratio of cell nuclei to cytoplasm increases with increasing cell malignancy. As an initial step in the development of AI-assisted cytology, we investigated two methods for the automatic detection of cell nuclei in blue-stained cells in cytopreparation images.
methodsWe evaluated the usefulness of the sliding window method (SWM) and mask region-based convolutional neural network (Mask-RCNN) in identifying the cell nuclei in oral cytopreparation images. Thirty cases of liquid-based oral cytology were analyzed. First, we performed the SWM by dividing each image into 96 × 96 pixels. Overall, 591 images with or without blue-stained cell nuclei were prepared as the training data and 197 as the test data (total: 1,576 images). Next, we performed the Mask-RCNN by preparing 130 images of Class II and III lesions and creating mask images showing cell regions based on these images.
resultsUsing the SWM method, the highest detection rate for blue-stained cells in the evaluation group was 0.9314. For Mask-RCNN, 37 cell nuclei were identified, and 1 cell nucleus was identified as a non-nucleus after 40 epochs (error rate:0.027).
conclusionsMask-RCNN is more accurate than SWM in identifying the cell nuclei. If the blue-stained cell nuclei can be correctly identified automatically, the entire cell morphology can be grasped faster, and the diagnostic performance of cytology can be improved.
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