CNN Epithelial Layer Detector for Digital Pathology
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Solution Overview
Problem
Existing methods for identifying epithelial portions of a tissue sample in digital pathology are limited in flexibility and accuracy, particularly in distinguishing epithelial layers from non-epithelial areas in breast and gastric cancer diagnosis, as they rely on hand-designed heuristics and cannot recognize non-nuclear materials within the epithelial layer.
Innovation Solution
A method utilizing a convolutional neural network (CNN) with a color transformation process to separate dye channels and determine whether tissue samples are inside or outside epithelial layers, trained using hand-labeled biopsy images and stochastic gradient descent for parameter adjustment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If hand-designed heuristics and color segmentation methods are used to identify epithelial portions, then the system can recognize nuclear material, but it cannot recognize non-nuclear materials such as mucinous cell bodies of goblet cells
Solution Approach 1:
The patent replaces hand-designed heuristic rules with a convolutional neural network (CNN) that automatically learns features from training data. This substitution enables the system to recognize both nuclear and non-nuclear materials by learning from labeled examples rather than relying on predefined rules that cannot adapt to diverse tissue structures.
Solution Approach 2:
The patent transforms the approach from fixed heuristic parameters to learned parameters through training. The CNN model adjusts its internal parameters (weights and biases) based on training data, allowing it to adapt to different tissue types and recognize various epithelial structures including mucinous cell bodies that were previously unrecognized.
2Productivity
If linear classifiers and hand-designed heuristics are used, then the system can classify tissue categories, but the methods require a new set of rules to be designed for each pattern that is to be recognized
Solution Approach 1:
The patent implements a universal CNN-based system that can handle multiple tissue classification patterns without requiring separate rule sets. The single trained model performs multiple classification tasks (identifying epithelial portions, stromal tissue, glandular structures, etc.) simultaneously, eliminating the need to design new rules for each pattern while maintaining high classification speed.
3Measurement precision
If color segmentation is applied to identify epithelial nuclei, then nuclear material can be recognized, but other materials in the epithelial layer are excluded from recognition
Solution Approach 1:
The patent moves beyond single-color or single-feature segmentation by using a multi-channel CNN that processes spatial and contextual information across the entire image. This dimensional expansion allows the system to recognize various materials (nuclear, cytoplasmic, extracellular) by analyzing patterns across multiple spatial scales and feature dimensions simultaneously.
Data Source
AI summary
An epithelial detector and method for automatically identifying epithelial portions of a tissue sample, includes: staining the tissue sample with at least two dyes; applying a color transformation to a color image of the tissue sample to obtain one or more color channels; and applying a trained convolutional neural network to the color channels to obtain a decision for position in the tissue as to whether it is inside or outside an epithelial layer. Also, a method for training the convolutional neural network.