Search

Background: Cervical intraepithelial neoplasia (CIN) is regarded as a potential precancerous state of the uterine cervix. Timely and appropriate early treatment of CIN can help reduce cervical cancer mortality. Accurate estimation of CIN grade correlated with human papillomavirus type, which is the primary cause of the disease, helps determine the patient's risk for developing the disease. Colposcopy is used to select women for biopsy. Expert pathologists examine the biopsied cervical epithelial tissue under a microscope. The examination can take a long time and is prone to error and often results in high inter-and intra-observer variability in outcomes. Methodology: We propose a novel image analysis toolbox that can automate CIN diagnosis using whole slide image (digitized biopsies) of cervical tissue samples. The toolbox is built as a four-step deep learning model that detects the epithelium regions, segments the detected epithelial portions, analyzes local vertical segment regions, and finally classifies each epithelium block with localized attention. We propose an epithelium detection network in this study and make use of our earlier research on epithelium segmentation and CIN classification to complete the design of the end-to-end CIN diagnosis toolbox. Results: The results show that automated epithelium detection and segmentation for CIN classification yields comparable results to manually segmented epithelium CIN classification. Conclusion: This highlights the potential as a tool for automated digitized histology slide image analysis to assist expert pathologists. [ABSTRACT FROM AUTHOR]

Background: Automated pathology techniques for detecting cervical cancer at the premalignant stage have advantages for women in areas with limited medical resources. Methods: This article presents EpithNet, a deep learning approach for the critical step of automated epithelium segmentation in digitized cervical histology images. EpithNet employs three regression networks of varying dimensions of image input blocks (patches) surrounding a given pixel, with all blocks at a fixed resolution, using varying network depth. Results: The proposed model was evaluated on 311 digitized histology epithelial images and the results indicate that the technique maximizes region-based information to improve pixel-wise probability estimates. EpithNet-mc model, formed by intermediate concatenation of the convolutional layers of the three models, was observed to achieve 94% Jaccard index (intersection over union) which is 26.4% higher than the benchmark model. Conclusions: EpithNet yields better epithelial segmentation results than state-of-the-art benchmark methods. [ABSTRACT FROM AUTHOR]

Background: Automated pathology techniques for detecting cervical cancer at the premalignant stage have advantages for women in areas with limited medical resources. Methods: This article presents EpithNet, a deep learning approach for the critical step of automated epithelium segmentation in digitized cervical histology images. EpithNet employs three regression networks of varying dimensions of image input blocks (patches) surrounding a given pixel, with all blocks at a fixed resolution, using varying network depth. Results: The proposed model was evaluated on 311 digitized histology epithelial images and the results indicate that the technique maximizes region-based information to improve pixel-wise probability estimates. EpithNet-mc model, formed by intermediate concatenation of the convolutional layers of the three models, was observed to achieve 94% Jaccard index (intersection over union) which is 26.4% higher than the benchmark model. Conclusions: EpithNet yields better epithelial segmentation results than state-of-the-art benchmark methods. [ABSTRACT FROM AUTHOR]

Background: The Paris System for Reporting Urinary Cytology represents a significant improvement in classification of urinary specimens. The system acknowledges the difficulty in cytologically diagnosing low-grade urothelial carcinomas and has developed categories to deal with this issue. The system uses six categories: unsatisfactory, negative for high-grade urothelial carcinoma (NHGUC), atypical urothelial cells, suspicious for high-grade urothelial carcinoma, high-grade urothelial carcinoma, other malignancies and a seventh subcategory (low-grade urothelial neoplasm). Methods: Three hundred and fifty-seven urine specimens were independently reviewed by four cytopathologists unaware of the previous diagnoses. Each cytopathologist rendered a diagnosis according to the Paris System categories. Agreement was assessed using absolute agreement and weighted chance-corrected agreement (kappa). Disagreements were classified as low impact and high impact based on the potential impact of a misclassification on clinical management. Results: The average absolute agreement was 65% with an average expected agreement of 44%. The average chance-corrected agreement (kappa) was 0.32. Nine hundred and ninety-nine of 1902 comparisons between rater pairs were in agreement, but 12% of comparisons differed by two or more categories for the category NHGUC. Approximately 15% of the disagreements were classified as high clinical impact. Conclusions: Our findings indicated that the scheme recommended by the Paris System shows adequate precision for the category NHGUC, but the other categories demonstrated unacceptable interobserver variability. This low level of diagnostic precision may negatively impact the applicability of the Paris System for widespread clinical application. [ABSTRACT FROM AUTHOR]