Automated cervical digitized histology whole-slide image analysis toolbox

• Main Authors: Sudhir Sornapudi (Author), Ravitej Addanki (Author), R Joe Stanley (Author), William V Stoecker (Author), Rodney Long (Author), Rosemary Zuna (Author), Shellaine R Frazier (Author), Sameer Antani (Author)
• Format: Book
• Published: Elsevier, 2021-01-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 2153-3539; 2153-3539; 10.4103/jpi.jpi_52_20
• Keywords: cervical cancer; cervical intraepithelial neoplasia; classification; convolutional neural networks; detection; digital pathology; histology; segmentation; whole slide image; Computer applications to medicine. Medical informatics; R858-859.7; Pathology; RB1-214
• Source: Journal of Pathology Informatics, Vol 12, Iss 1, Pp 26-26 (2021)

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.