抄録
In Digital Pathology, histological slides digitized allow the use of techniques for automatic procedures in histopathology, permitting the automatic quantification of the rate of sclerosed glomeruli to order the biopsy slides so that the most serious cases can be identified more quickly. In this work, we evaluate the YOLOv3 as deep neural network to identify glomeruli in WSI and classify in functional and sclerosed glomeruli. This work used the framework YOLOv3, with 53-layers convolutional neural network, and 30 complete slides from the Bio-Atlas repository (Pennsylvania State University), which resulted in 2448 images of 1024x1024 pixels with one or more glomeruli, used for training and performance evaluation. A total of 585 sclerosed glomeruli and 3383 functional glomeruli were labeled. Through the experiments, we achieve high performance in identification and classification of glomeruli (e.g., recall of 96.8%, precision of 95.9%, accuracy of 98.1%, and an F1 score of 96.3%). The method is capable to identify and report the location of the glomeruli on the slide, classify the glomeruli in functional and sclerosed, and precisely provide the percentage of sclerosed glomeruli, allowing support for a histopathological study of kidney diseases in the medical routine.
