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J. Imaging 2018, 4(7), 91; https://doi.org/10.3390/jimaging4070091

Faster R-CNN-Based Glomerular Detection in Multistained Human Whole Slide Images

1
Department of Healthcare Information Management, The University of Tokyo Hospital, Tokyo 113-8655, Japan
2
Department of Biomedical Informatics, Graduate School of Medicine, The University of Tokyo, Tokyo 113-8655, Japan
3
Department of Pathology, Graduate School of Medicine, The University of Tokyo, Tokyo 113-8655, Japan
4
Department of Pathology, Teikyo University School of Medicine, Tokyo 173-8605, Japan
*
Author to whom correspondence should be addressed.
Received: 16 May 2018 / Revised: 13 June 2018 / Accepted: 2 July 2018 / Published: 4 July 2018
(This article belongs to the Special Issue Medical Image Analysis)
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Abstract

The detection of objects of interest in high-resolution digital pathological images is a key part of diagnosis and is a labor-intensive task for pathologists. In this paper, we describe a Faster R-CNN-based approach for the detection of glomeruli in multistained whole slide images (WSIs) of human renal tissue sections. Faster R-CNN is a state-of-the-art general object detection method based on a convolutional neural network, which simultaneously proposes object bounds and objectness scores at each point in an image. The method takes an image obtained from a WSI with a sliding window and classifies and localizes every glomerulus in the image by drawing the bounding boxes. We configured Faster R-CNN with a pretrained Inception-ResNet model and retrained it to be adapted to our task, then evaluated it based on a large dataset consisting of more than 33,000 annotated glomeruli obtained from 800 WSIs. The results showed the approach produces comparable or higher than average F-measures with different stains compared to other recently published approaches. This approach could have practical application in hospitals and laboratories for the quantitative analysis of glomeruli in WSIs and, potentially, lead to a better understanding of chronic glomerulonephritis. View Full-Text
Keywords: glomerulus detection; digital pathology; whole slide images; deep neural network glomerulus detection; digital pathology; whole slide images; deep neural network
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Kawazoe, Y.; Shimamoto, K.; Yamaguchi, R.; Shintani-Domoto, Y.; Uozaki, H.; Fukayama, M.; Ohe, K. Faster R-CNN-Based Glomerular Detection in Multistained Human Whole Slide Images. J. Imaging 2018, 4, 91.

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