Deep Learning Cross-Matching Image Processing
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Solution Overview
Problem
Current HLA cross-matching methods, particularly complement-dependent cytotoxicity (CDC) tests, are time-consuming and labor-intensive, requiring seven to eight days to complete and are prone to worker variability due to the manual counting of dead and live cells under a fluorescence microscope.
Innovation Solution
A deep learning-based method and apparatus that preprocesses cross-matching images to create labeled images, generates uncertainty-aware weight maps, and performs P-Net learning to distinguish live and dead cells efficiently, reducing the time required for cross-matching and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual counting of dead and live cells under fluorescence microscope is used in CDC test, then measurement capability is achieved, but productivity is low and time consumption is high (7-8 days)
Solution Approach 1:
The patent replaces the manual mechanical counting process under fluorescence microscope with an automated image processing system using deep learning algorithms. The system captures images of cross-matching wells and automatically identifies, classifies, and counts dead and live cells through neural network-based image recognition, eliminating the need for manual observation and counting by technicians.
Solution Approach 2:
The patent creates digital copies of the cross-matching images through photography or digital capture from fluorescence microscopes, and then processes these copies using automated algorithms. This allows multiple analyses of the same sample without requiring additional physical samples or extending the test duration, enabling rapid re-analysis and verification.
2Measurement precision
If manual cell counting is performed by workers, then measurement capability is achieved, but measurement precision is low due to worker variability and skill differences
Solution Approach 1:
The patent replaces human workers with an automated deep learning-based image processing system that consistently applies the same classification criteria to all images. The system uses trained neural networks to identify cell boundaries, nuclear staining patterns, and morphological features, eliminating variability introduced by different workers' skills, fatigue, or subjective judgment.
Solution Approach 2:
The system incorporates feedback mechanisms where the deep learning model is continuously trained and refined using labeled training data from expert-annotated images. The model learns from correct and incorrect classifications, adjusting its parameters to improve accuracy over time. This feedback loop ensures the system achieves and maintains high measurement precision through iterative optimization.
3Productivity
If deep learning-based image processing is implemented, then productivity is improved and time is reduced, but device complexity increases
Solution Approach 1:
The patent employs a universal deep learning framework that can process multiple types of cross-matching images and perform various analysis tasks (cell detection, classification, counting, and ratio calculation) using the same core system. The modular architecture allows the system to handle different image formats, microscope types, and staining protocols through configurable parameters rather than requiring separate specialized systems for each function.
Data Source
AI summary
The present disclosure relates to method and apparatus for processing cross matching image based on deep learning.