Deep Learning-Based Microscopic Diagnosis of Odontogenic Keratocysts and Non-Keratocysts in Haematoxylin and Eosin-Stained Incisional Biopsies

• Published in: Diagnostics (Basel) Vol. 11; no. 12; p. 2184
• Main Authors: Rao, Roopa S, Shivanna, Divya B, Mahadevpur, Kirti S, Shivaramegowda, Sinchana G, Prakash, Spoorthi, Lakshminarayana, Surendra, Patil, Shankargouda
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 24.11.2021; MDPI
• Subjects: Algorithms; Artificial intelligence; Automation; Biopsy; Cysts; Datasets; Deep learning; dentigerous cysts; Histopathology; histopathology images; image classification; Inflammation; Jaw; Machine learning; Neural networks; odontogenic keratocysts; Pathology; radicular cysts; deep learning; radicular cysts; dentigerous cysts; odontogenic keratocysts; image classification; histopathology images
• ISSN: 2075-4418; 2075-4418
• DOI: 10.3390/diagnostics11122184

The goal of the study was to create a histopathology image classification automation system that could identify odontogenic keratocysts in hematoxylin and eosin-stained jaw cyst sections. From 54 odontogenic keratocysts, 23 dentigerous cysts, and 20 radicular cysts, about 2657 microscopic pictures with 400× magnification were obtained. The images were annotated by a pathologist and categorized into epithelium, cystic lumen, and stroma of keratocysts and non-keratocysts. Preprocessing was performed in two steps; the first is data augmentation, as the Deep Learning techniques (DLT) improve their performance with increased data size. Secondly, the epithelial region was selected as the region of interest. Four experiments were conducted using the DLT. In the first, a pre-trained VGG16 was employed to classify after-image augmentation. In the second, DenseNet-169 was implemented for image classification on the augmented images. In the third, DenseNet-169 was trained on the two-step preprocessed images. In the last experiment, two and three results were averaged to obtain an accuracy of 93% on OKC and non-OKC images. The proposed algorithm may fit into the automation system of OKC and non-OKC diagnosis. Utmost care was taken in the manual process of image acquisition (minimum 28-30 images/slide at 40× magnification covering the entire stretch of epithelium and stromal component). Further, there is scope to improve the accuracy rate and make it human bias free by using a whole slide imaging scanner for image acquisition from slides.