Deep learning approach for classification of chondrocytes in rats

서지정보

ㆍ발행기관 : 충북대학교 동물의학연구소 ㆍ수록지정보 : Journal of Biomedical and Translational Research / 23권 / 1호
ㆍ저자명 : Jin Seok Kang

목차

Abstract
INTRODUCTION
MATERIALS AND METHODS
Data collection
Deep learning
Slide annotation
Visualization of predicted patches
RESULTS
Collection of training and evaluation data
Inception-v3 classification of chondrocytes and non-chondrocytes
Visualization of chondrocytes and non-chondrocytes
DISCUSSION
REFERENCES

영어 초록

The rapid development of computer vision and deep learning has enabled these technologies to be applied to the automated classification and counting of microscope images, thereby relieving of some burden from pathologists in terms of performing tedious microscopic examination for analysis of a large number of slides for pathological lesions. Recently, the use of these digital methods has expanded into the field of medical image analysis. In this study, the Inception-v3 deep learning model was used for classification of chondrocytes from knee joints of rats. Knee joints were extracted, fixed in neutral buffered formalin, decalcified, processed and embedded in paraffin, and hematoxylin and eosin (H&E) stained. The H&E stained slides were converted into whole slide imaging (WSI), and the images were cropped to 79 × 79 pixels. The images were divided into training (60.42%) and test (39.58%) sets (46,349 and 30,360 images, respectively). Then, images containing chondrocytes were classified by Inception-v3 and accuracy was calculated. We visualized the images containing chondrocytes in WSIs by adding colored dots to patches. When images of chondrocytes in knee joints were evaluated, the accuracy was within the range of 91.20 ± 8.43%. Therefore, it is considered that the Inception-v3 deep learning model was able to distinguish chondrocytes from non-chondrocytes in knee joints of rats with a relatively high accuracy. The above results taken together confirmed that this deep learning model could classify the chondrocytes and this promising approach will provide pathologists a fast and accurate analysis of diverse tissue structures.

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