ArticlePloS one2013
Semantic focusing allows fully automated single-layer slide scanning of cervical cytology slides.
Article in PloS one, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Validation of Digital Cytology for Primary Diagnosis Across a Range of Specimen Types.Cytopathology : official journal of the British Society for Clinical Cytology · 2026Article
- Digitizing microscope slide-based natural history collections: A protocol using slide scanner technology.PloS one · 2026Article
- Deep learning enabled liquid-based cytology model for cervical precancer and cancer detection.Nature communications · 2025Article
- Assessing the quality of whole slide images in cytology from nuclei features.Journal of pathology informatics · 2025Article
- Closing the Automation Gap: Robust AI for Dual-Stain Cervical Cancer Screening Triage.Research square · 2025Article
- Deep Learning-Based Screening of Urothelial Carcinoma in Whole Slide Images of Liquid-Based Cytology Urine Specimens.Cancers · 2022Article
- Malaria Detection Accelerated: Combing a High-Throughput NanoZoomer Platform with a ParasiteMacro Algorithm.Pathogens (Basel, Switzerland) · 2022Article
- A Deep Learning Model for Cervical Cancer Screening on Liquid-Based Cytology Specimens in Whole Slide Images.Cancers · 2022Article
- Role of immunocytochemistry in cervical cancer screening.CytoJournal · 2022Review
- Accuracy and Efficiency of Deep-Learning-Based Automation of Dual Stain Cytology in Cervical Cancer Screening.Journal of the National Cancer Institute · 2021Article
- Video compression to support the expansion of whole-slide imaging into cytology.Journal of medical imaging (Bellingham, Wash.) · 2019Article
- Whole-Slide Image Focus Quality: Automatic Assessment and Impact on AI Cancer Detection.Journal of pathology informatics · 2019Article
- Application of p16/Ki-67 dual-staining cytology in cervical cancers.Journal of Cancer · 2019Review
- Towards machine learned quality control: A benchmark for sharpness quantification in digital pathology.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2018Article
- DeepFocus: Detection of out-of-focus regions in whole slide digital images using deep learning.PloS one · 2018Article
- Conventional Microscopical versus Digital Whole-Slide Imaging-Based Diagnosis of Thin-Layer Cervical Specimens: A Validation Study.Journal of pathology informatics · 2018Article
- Triage of HPV positive women in cervical cancer screening.Journal of clinical virology : the official publication of the Pan American Society for Clinical Virology · 2016Review
- Working toward consensus among professionals in the identification of classical cervical cytomorphological characteristics in whole slide images.Journal of pathology informatics · 2015Article
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5 authors.
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Abstract
Liquid-based cytology (LBC) in conjunction with Whole-Slide Imaging (WSI) enables the objective and sensitive and quantitative evaluation of biomarkers in cytology. However, the complex three-dimensional distribution of cells on LBC slides requires manual focusing, long scanning-times, and multi-layer scanning. Here, we present a solution that overcomes these limitations in two steps: first, we make sure that focus points are only set on cells. Secondly, we check the total slide focus quality. From a first analysis we detected that superficial dust can be separated from the cell layer (thin layer of cells on the glass slide) itself. Then we analyzed 2,295 individual focus points from 51 LBC slides stained for p16 and Ki67. Using the number of edges in a focus point image, specific color values and size-inclusion filters, focus points detecting cells could be distinguished from focus points on artifacts (accuracy 98.6%). Sharpness as total focus quality of a virtual LBC slide is computed from 5 sharpness features. We trained a multi-parameter SVM classifier on 1,600 images. On an independent validation set of 3,232 cell images we achieved an accuracy of 94.8% for classifying images as focused. Our results show that single-layer scanning of LBC slides is possible and how it can be achieved. We assembled focus point analysis and sharpness classification into a fully automatic, iterative workflow, free of user intervention, which performs repetitive slide scanning as necessary. On 400 LBC slides we achieved a scanning-time of 13.9±10.1 min with 29.1±15.5 focus points. In summary, the integration of semantic focus information into whole-slide imaging allows automatic high-quality imaging of LBC slides and subsequent biomarker analysis.
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