Gloria Bueno1, M Milagro Fernandez-Carrobles2, Lucia Gonzalez-Lopez3, Oscar Deniz2. 1. University of Castilla-La Mancha, ETSI Industriales, VISILAB, Ciudad Real, Spain. Electronic address: gloria.bueno@uclm.es. 2. University of Castilla-La Mancha, ETSI Industriales, VISILAB, Ciudad Real, Spain. 3. Hospital General Universitario de Ciudad Real, Ciudad Real, Spain.
Abstract
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.
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.
Authors: Xiang Li; Richard C Davis; Yuemei Xu; Zehan Wang; Nao Souma; Gina Sotolongo; Jonathan Bell; Matthew Ellis; David Howell; Xiling Shen; Kyle J Lafata; Laura Barisoni Journal: J Med Imaging (Bellingham) Date: 2021-12-20
Authors: Ethan H Nguyen; Haichun Yang; Ruining Deng; Yuzhe Lu; Zheyu Zhu; Joseph T Roland; Le Lu; Bennett A Landman; Agnes B Fogo; Yuankai Huo Journal: IEEE Trans Med Imaging Date: 2022-03-02 Impact factor: 10.048
Authors: Nassim Bouteldja; Barbara M Klinkhammer; Roman D Bülow; Patrick Droste; Simon W Otten; Saskia Freifrau von Stillfried; Julia Moellmann; Susan M Sheehan; Ron Korstanje; Sylvia Menzel; Peter Bankhead; Matthias Mietsch; Charis Drummer; Michael Lehrke; Rafael Kramann; Jürgen Floege; Peter Boor; Dorit Merhof Journal: J Am Soc Nephrol Date: 2020-11-05 Impact factor: 10.121