期刊
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
卷 184, 期 -, 页码 -出版社
ELSEVIER IRELAND LTD
DOI: 10.1016/j.cmpb.2019.105273
关键词
Semantic segmentation; Deep learning; Consecutive segmentation-classification; CNN; Digital pathology; Glomeruli detection; Sclerotic glomeruli; Segnet; U-Net
类别
资金
- [612471]
Background and Objective: 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. Methods: 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. Results: 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. Conclusion: 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. (C) 2019 Elsevier B.V. All rights reserved.
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