Multi-view features integrated 2D\3D Net for glomerulopathy histologic types classification using ultrasound images. (November 2021)

• Record Type: Journal Article
• Authors: Hai, Jinjin; Qiao, Kai; Chen, Jian; Liang, Ningning; Zhang, Lijie; Yan, Bin
• Is Part Of: Computer methods and programs in biomedicine. Volume 212(2021)
• Publication Date: 2021-11
• Subjects: Renal ultrasound -- Histologic types -- Group convolution -- 3D convolution -- Multi-view features; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2021.106439
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Noninvasive and convenient ultrasound images are used for discriminating the histologic type of glomerulopathy instead of pathological images by renal biopsy. A multi-view and cross-domain integration strategy is proposed to obtain more effective renal features and improve diagnosis accuracy. Group convolution and 3D convolution are firstly deployed to process 3D data input and integrate multi-view features of renal ultrasound images. Cross-domain concatenation in each spatial resolution of feature maps is applied for more informative feature learning. Compared results demonstrate that our designed network achieves the best performance and has great superiority on histologic type diagnosis. Abstract: Background and Objective: Early diagnoses and rational therapeutics of glomerulopathy can control progression and improve prognosis. The gold standard for the diagnosis of glomerulopathy is pathology by renal biopsy, which is invasive and has many contraindications. We aim to use renal ultrasonography for histologic classification of glomerulopathy. Methods: Ultrasonography can present multi-view sections of kidney, thus we proposed a multi-view and cross-domain integration strategy (CD-ConcatNet) to obtain more effective features and improve diagnosis accuracy. We creatively apply 2D group convolution and 3D convolution to process multiple 2D ultrasound images and extract multi-view features of renal ultrasound images. Cross-domain concatenation in each spatial resolution of feature maps is applied for more informative feature learning. Results: A total of 76 adult patients were collected and divided into training dataset (56 cases with 515 images) and validation dataset (20 cases with 180 images). We obtained the best mean accuracy of 0.83 and AUC of 0.8667 in the validation dataset. Conclusion: Comparison experiments demonstrate that our designed CD-ConcatNet achieves the best classification performance and has great superiority on histologic types diagnosis. Results also prove that the integration of multi-view ultrasound images is beneficial for histologic classification and ultrasound images can indeed provide discriminating information for histologic diagnosis.