Glomerulosclerosis identification in whole slide images using semantic segmentation.

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ID: 72934
2019
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Abstract
Glomeruli identification, i.e., detection and characterization, is a key procedure in many nephropathology studies. In this paper, semantic segmentation based on convolutional neural networks (CNN) is proposed to detect glomeruli using Whole Slide Imaging (WSI) follows by a classification CNN to divide the glomeruli into normal and sclerosed.Comparison between U-Net and SegNet CNNs is performed for pixel-level segmentation considering both a two and three class problem, that is, a) non-glomerular and glomerular structures and b) non-glomerular normal glomerular and sclerotic structures. The two class semantic segmentation result is then used for a CNN classification where glomerular regions are divided into normal and global sclerosed glomeruli.These methods were tested on a dataset composed of 47 WSIs belonging to human kidney sections stained with Periodic Acid Schiff (PAS). The best approach was the SegNet for two class segmentation follows by a fine-tuned AlexNet network to characterize the glomeruli. 98.16% of accuracy was obtained with this process of consecutive CNNs (SegNet-AlexNet) for segmentation and classification.The results obtained demonstrate that the sequential CNN segmentation-classification strategy achieves higher accuracy reducing misclassified cases and therefore being the methodology proposed for glomerulosclerosis detection.
Reference Key
bueno2019glomerulosclerosiscomputer Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Bueno, Gloria;Fernandez-Carrobles, M Milagro;Gonzalez-Lopez, Lucia;Deniz, Oscar;
Journal computer methods and programs in biomedicine
Year 2019
DOI
S0169-2607(19)31138-1
URL
Keywords
Deep learning digital pathology semantic segmentation u-net consecutive segmentation-classification cnn glomeruli detection sclerotic glomeruli segnet

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