Multi-view features integrated 2D\3D Net for glomerulopathy histologic types classification using ultrasound images
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Updated: Oct 15, 2025
Author Spotlight: Aiding Research in Kidney Biology by Labeling Glomeruli in Cleared Tissues
Published on: February 9, 2024
Jinjin Hai1, Kai Qiao1, Jian Chen1
1Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategy Support Force Information Engineering University, China.
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.
