Cell Segmentation Masks Using Repel Coding for Clustered Pathology Images
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Solution Overview
Problem
Existing machine learning techniques for digital pathology image analysis face challenges in accurately detecting and classifying cells due to varying cell morphologies, clustering, and imaging protocols, which complicates cell detection and classification, especially for immune cells.
Innovation Solution
Employing repel coding to generate masks for cell centers with increased response valleys between neighboring cells, enhancing the training of convolutional neural networks to improve cell detection and classification by emphasizing cell centers and reducing emphasis on pixel peripheries, using a modified U-Net architecture for segmentation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional machine learning techniques are used for cell detection and classification, then the system can process digital pathology images, but the accuracy is reduced due to varying cell morphologies, clustering, and imaging protocols
Solution Approach 1:
The patent transforms cell localization labels from point coordinates to spatial density maps using repel coding, changing the parameter representation from discrete points to continuous spatial distributions. This allows the machine learning model to learn robust features that are invariant to variations in cell morphology and imaging protocols while maintaining high detection accuracy.
Solution Approach 2:
The patent introduces repel coding as an intermediary transformation step between the raw cell labels and the training data for the machine learning model. This intermediary process converts point-based cell centers into spatial density maps that encode both location and contextual information, enabling the model to better handle varying morphologies and imaging conditions.
2Ease of manufacture
If point-based cell labels are used for training, then the training process is simple, but the model fails to capture spatial relationships and cell periphery information
Solution Approach 1:
The patent transitions from one-dimensional point labels to two-dimensional spatial density maps through repel coding. This dimensional transformation preserves the simplicity of point-based annotation while enriching the training data with spatial relationship information and cell periphery context, eliminating information loss without complicating the labeling process.
3Device complexity
If standard convolutional neural networks are used for segmentation, then the architecture is straightforward, but the model struggles with clustered cells and varying morphologies
Solution Approach 1:
The patent changes the training data parameters from point coordinates to spatial density maps using repel coding, which encodes spatial relationships and cell boundaries into the training labels. This parameter transformation enables standard CNN architectures to achieve high segmentation accuracy for clustered cells with varying morphologies without increasing architectural complexity.
Data Source
AI summary
The present disclosure relates to computer-implement techniques for cell localization and classification. Particularly, aspects of the present disclosure are directed to accessing an image for a biological sample, where the image depicts cells comprising a staining pattern of a biomarker; inputting the image into a machine learning model; encoding, by the machine learning model, the image into a feature representation comprising extracted discriminative features; combining, by the machine learning model, feature and spatial information of the cells and the staining pattern of the biomarker through a sequence of up-convolutions and concatenations with the extracted discriminative features from the feature representation; and generating, by the machine learning model, two or more segmentation masks for the biomarker in the image based on the combined feature and spatial information of the cells and the staining pattern of the biomarker.