Cell Localization Models Using Repel Coding for Dense Image Segmentation
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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 density, which complicates boundary definition and classification, especially in immunofluorescence-stained images.
Innovation Solution
The use of 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 peripheral emphasis, thereby improving the accuracy of segmentation and classification.
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 density that complicate boundary definition
Solution Approach 1:
The patent applies segmentation by dividing the cell detection task into distinct components: first identifying cell centers using repel coding, then separately defining cell boundaries through distance transform operations. This segmentation approach resolves the boundary definition difficulty by treating center detection and boundary delineation as separate processing stages rather than attempting simultaneous detection.
Solution Approach 2:
The patent implements local quality by applying repel coding specifically at cell center locations rather than uniformly across the entire image. The repel coding creates localized response peaks at cell centers with suppressed responses in inter-cellular regions, allowing the system to focus computational resources on critical detection points while adapting to varying local cell densities and morphologies.
2Measurement precision
If conventional training methods are used without repel coding, then the training process is simpler, but the machine learning model fails to accurately emphasize cell centers and distinguish neighboring cells in dense regions
Solution Approach 1:
The patent applies preliminary action by pre-processing the training images with repel coding before feeding them to the machine learning model. The repel coding is applied in advance to generate enhanced feature maps that emphasize cell centers and suppress peripheral regions, so that when the model trains, the cell center locations are already highlighted, improving detection accuracy without requiring the model to learn these patterns from scratch.
3Measurement precision
If standard labeling approaches are used for training data, then the labeling process is more straightforward, but the model cannot effectively learn to distinguish cell centers from peripheral regions in clustered cell populations
Solution Approach 1:
The patent applies the color changes principle by transforming the spatial information into distinct intensity patterns through repel coding. Cell centers are assigned high intensity values (emphasized), while peripheral and inter-cellular regions receive suppressed values. This transformation converts spatial relationship information into a format that preserves and emphasizes critical distinctions between cell centers and surrounding areas, preventing information loss during the labeling process.
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.