Deep Learning Renal Biopsy Segmentation for Nephropathology
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Solution Overview
Problem
Renal biopsy interpretation is time-consuming and limited by poor reproducibility due to the variability in tissue preparations and stains used, which complicates the application of machine learning approaches for large-scale tissue quantification in nephrology.
Innovation Solution
The development and deployment of deep learning models, specifically Convolutional Neural Networks (CNNs), for segmenting histological primitives in renal biopsies stained with multiple stains, utilizing optimal digital magnifications and data augmentation techniques to improve model performance across diverse pathology laboratories.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual assessment and visual quantification by pathologists are used, then disease diagnosis and staging can be performed, but the process is time-consuming and has poor intra- and inter-reader reproducibility
Solution Approach 1:
The patent replaces the manual mechanical assessment system with an automated deep learning-based image analysis system. Convolutional neural networks process renal biopsy images to automatically segment and quantify histological structures, eliminating the time-consuming manual evaluation while improving measurement precision and reproducibility through consistent algorithmic application across all cases.
Solution Approach 2:
The deep learning model performs self-service by automatically analyzing renal biopsy images without requiring pathologist intervention for each case. The system independently segments histological primitives, quantifies structures, and generates diagnostic information, thereby reducing time loss while maintaining high measurement precision through its trained computational capabilities.
2Productivity
If machine learning approaches are applied for large-scale tissue quantification, then efficiency is improved, but heterogeneity of preparations and stains across multiple centers complicates model performance
Solution Approach 1:
The patent applies parameter changes by training the deep learning model on diverse datasets encompassing multiple stain types (H&E, PAS, Trichrome, Silver) and various preparation conditions from different centers. The model learns to adapt to different staining parameters and preparation variations, maintaining reliable performance across heterogeneous inputs while preserving high productivity through automated analysis.
Solution Approach 2:
The deep learning model achieves universality by being trained to handle multiple stain types and preparation variations simultaneously. A single model architecture processes diverse renal biopsy images from multiple centers with different staining protocols, ensuring consistent and reliable performance across varied conditions while maintaining high efficiency for large-scale quantification.
3Adaptability or versatility
If deep learning models are trained on multiple stain types, then versatility across different pathology preparations is improved, but model complexity increases
Solution Approach 1:
The patent segments the complex multi-stain analysis task into separate specialized models, each trained on a specific stain type (H&E, PAS, Trichrome, Silver). This segmentation approach maintains high versatility by covering multiple stain types while reducing individual model complexity, as each model focuses on learning from a single stain's characteristics rather than attempting to generalize across all stains simultaneously.
Data Source
AI summary
Embodiments discussed herein facilitate segmentation of histological primitives from stained histology of renal biopsies via deep learning and/or training deep learning model(s) to perform such segmentation. One example embodiment is configured to access a first histological image of a renal biopsy comprising a first type of histological primitives, wherein the first histological image is stained with a first type of stain; provide the first histological image to a first deep learning model trained based on the first type of histological primitive and the first type of stain; and receive a first output image from the first deep learning model, wherein the first type of histological primitives is segmented in the first output image.