Deep learning-based glomerulus detection and classification with generative morphology augmentation in renal pathology images.
Chia-Feng Juang, Ya-Wen Chuang, Guan-Wen Lin, I-Fang Chung, Ying-Chih Lo
Author Information
Chia-Feng Juang: Department of Electrical Engineering, National Chung Hsing University, Taichung 40227, Taiwan, ROC.
Ya-Wen Chuang: Section of Nephrology, Department of Medicine, Taichung Veterans General Hospital, Taichung 40705, Taiwan, ROC; Graduate Institute of Biomedical Sciences, College of Medicine, China Medical University, Taichung 406040, Taiwan, ROC; School of Medicine, College of Medicine, China Medical University, Taichung 406040, Taiwan, ROC; Department of Post-Baccalaureate Medicine, College of Medicine, National Chung Hsing University, Taichung 40227, Taiwan, ROC.
Guan-Wen Lin: Department of Electrical Engineering, National Chung Hsing University, Taichung 40227, Taiwan, ROC.
I-Fang Chung: Institute of Biomedical Informatics, National Yang Ming Chiao Tung University, Taipei 112304, Taiwan, ROC. Electronic address: ifchung@nycu.edu.tw.
Ying-Chih Lo: Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, MA 02115, USA; Harvard Medical School, Boston, MA 02115, USA. Electronic address: neversee.paul@gmail.com.
Glomerulus morphology on renal pathology images provides valuable diagnosis and outcome prediction information. To provide better care, an efficient, standardized, and scalable method is urgently needed to optimize the time-consuming and labor-intensive interpretation process by renal pathologists. This paper proposes a deep convolutional neural network (CNN)-based approach to automatically detect and classify glomeruli with different stains in renal pathology images. In the glomerulus detection stage, this paper proposes a flattened Xception with a feature pyramid network (FX-FPN). The FX-FPN is employed as a backbone in the framework of faster region-based CNN to improve glomerulus detection performance. In the classification stage, this paper considers classifications of five glomerulus morphologies using a flattened Xception classifier. To endow the classifier with higher discriminability, this paper proposes a generative data augmentation approach for patch-based glomerulus morphology augmentation. New glomerulus patches of different morphologies are generated for data augmentation through the cycle-consistent generative adversarial network (CycleGAN). The single detection model shows the F score up to 0.9524 in H&E and PAS stains. The classification result shows that the average sensitivity and specificity are 0.7077 and 0.9316, respectively, by using the flattened Xception with the original training data. The sensitivity and specificity increase to 0.7623 and 0.9443, respectively, by using the generative data augmentation. Comparisons with different deep CNN models show the effectiveness and superiority of the proposed approach.
