Epithelial Structure Detector Using Skeleton Graphs
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Solution Overview
Problem
Current methods for distinguishing and detecting epithelial structures in tissue samples are inadequate, as they rely on hand-designed heuristics and do not effectively capture the connectivity and relationships between nuclei, limiting their diagnostic accuracy.
Innovation Solution
A method is developed to train a classifier as an epithelial texture classifier by generating a skeleton graph from identified nuclei in biopsy tissue micrographs, using a graphical user interface to manually draw walls and select geodesic paths, and applying this data to train a convolutional neural network or support vector machine to detect epithelial structures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If hand-designed heuristics are used to classify tissue pixels into categories, then the classification process is simple and fast, but the detection accuracy of epithelial structures is insufficient
Solution Approach 1:
The system uses automatically generated skeleton graphs and machine learning classifiers to perform epithelial structure detection, eliminating the need for manual heuristics while maintaining simplicity in operation. The classifier learns from training data to automatically distinguish epithelial from non-epithelial regions.
Solution Approach 2:
The patent replaces hand-designed heuristic rules with a machine learning-based classification system. Instead of manually programmed thresholds and rules, the system uses trained classifiers (such as support vector machines or neural networks) that learn optimal classification boundaries from labeled training data, thereby improving detection accuracy while reducing reliance on expert knowledge.
2Loss of information
If existing classification methods are used to distinguish tissue types, then the process is straightforward, but the connectivity and relationships between nuclei are not effectively captured
Solution Approach 1:
The patent transforms the classification problem from pixel-level 2D image data to graph-based structural representation. By creating skeleton graphs that represent nuclei as nodes and their spatial relationships as edges, the system captures connectivity information in a new dimensional space, preserving topological relationships that would be lost in traditional pixel classification.
Solution Approach 2:
The skeleton graph serves as an intermediary representation between the raw image data and the final classification result. This intermediate structure explicitly models the connectivity and spatial relationships between nuclei, allowing the classifier to access structural information that directly encodes the relationships the patent aims to preserve.
3Productivity
If manual methods are used to identify epithelial structures, then the process requires minimal computational resources, but the productivity and scalability are limited
Solution Approach 1:
The system performs preliminary automated actions by generating skeleton graphs and applying trained classifiers to identify epithelial structures. This preliminary automated processing handles the bulk of the detection work, reducing the need for manual review while maintaining high accuracy. The automation can be further refined through iterative training on user-corrected results.
Solution Approach 2:
The system incorporates feedback mechanisms where classification results can be reviewed and corrected, and these corrections are used to retrain and improve the classifier. This feedback loop enables the system to learn from errors and continuously improve its performance, balancing automation with quality control.
Data Source
AI summary
A method for training a classifier to be operative as an epithelial texture classifier, includes obtaining a plurality of training micrograph areas of biopsy tissue and for each of the training micrograph areas, identifying probable locations of nuclei that form epithelia, generating a skeleton graph from the probable locations of the nuclei that form the epithelia, manually drawing walls on the skeleton graph outside of the epithelia to divide the epithelia from one another, and manually selecting points that lie entirely inside the epithelia to generate open and/or closed geodesic paths in the skeleton graph between pairs of the selected points. Data is obtained from points selected from the walls and the paths and applied to a classifier to train the classifier as the epithelial texture classifier. A method and detector for detecting epithelial structures includes applying a sample micrograph area of biopsy tissue to an epithelial texture classifier; identifying probable locations of nuclei that form epithelia of the sample micrograph area with the epithelial texture classifier, generating a skeleton graph from the probable locations of the nuclei that form the epithelia of the sample micrograph area, determining a set of open and/or closed geodesic paths in the skeleton graph of the sample micrograph area; and determining a set of the epithelial masks using the open and/or closed epithelial paths of the sample micrograph area.