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

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.

Semantic Segmentation Framework for Glomeruli Detection and Classification in Kidney Histological Sections

“…In the realm of digital pathology, several recent studies have proposed CAD systems for glomerulus identification and classification in renal biopsies [1][2][3][4][5][6][7][8]. The eligibility for transplantation of a kidney retrieved from Expanded Criteria Donors (ECD) relies on rush histological examination of the organ to evaluate suitability for transplant [9].…”

“…The obtained results showed that the CNN method outperformed the HOG and SVM classifier [1]. Kawazoe et al faced the task of glomeruli detection in multistained human kidney biopsy slides by using a Deep Learning approach based on Faster R-CNN [6]. Marsh et al developed a deep learning model that recognizes and classifies sclerotic and non-sclerotic glomeruli in whole-slide images of frozen donor kidney biopsies.…”

“…The analysis of the literature suggests that main works focused on the glomerular detection task only, without considering the further classification into sclerotic and non-sclerotic [1,2,4,6]. Few papers considered the assessment of global glomerulosclerosis from kidney biopsies [7,8,14].…”

To What Extent Does Downsampling, Compression, and Data Scarcity Impact Renal Image Analysis?

“…Although pre-processing and the amount of training data will strongly affect detection accuracy for automatic glomerular analysis, most literature about glomerulus classification and detection mention little about these problems. For example, Kawazoe et al [14] proposed a method of using Faster R-CNN to detect glomeruli on periodic acid-Schiff (PAS), periodic acidmethenamine silver (PAM), Masson trichrome (MT) and Azan stained renal WSIs. In their methods, images are taken by 40× objective lens magnification and detection is conducted with downsampling equivalent to 5× objective lens magnification.…”

Artificial intelligence system for detecting superficial laryngopharyngeal cancer with high efficiency of deep learning

“…Quite recently, evidence of a faster lesion detection approach is being reported due to advances in algorithms and metastasis learning in glomerular lesion and gastric cancer detection. 20,21 This study aimed to construct and evaluate an AI model for SLPC detection by using a highly efficient learning system.…”