Cell Classification via Membrane Blob Statistical Analysis
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Solution Overview
Problem
Current algorithms for cell classification in tissue images based on membrane features are inadequate, as they often miss nuclei or ignore regions with combined membrane and cytoplasmic staining, leading to incomplete or incorrect quantification and differentiation of cell types, especially with complex staining patterns relevant in immunotherapy.
Innovation Solution
A digital image processing system and method that includes a color deconvolution module, a Difference of Gaussian (DoG) filter, Otsu thresholding, and blob extraction to enhance and classify membrane staining patterns, computing statistical values for accurate cell identification and differentiation between elliptical and punctate membrane patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If nuclei detection is used to search for stained membrane in a predefined neighborhood, then cell classification can be performed, but nuclei may be missed or membrane regions outside the predefined radius may not be detected
Solution Approach 1:
The patent segments the image analysis process into distinct modules: nuclei detection module, membrane detection module, and classification module. The membrane detection operates independently of nuclei detection by analyzing the entire image, thus resolving the contradiction between relying on nuclei detection and missing membrane regions.
Solution Approach 2:
The patent introduces an intermediary approach where membrane regions are detected through color deconvolution and machine learning classification without requiring nuclei as reference points. This intermediary method bridges the gap between nuclei-based and region-based detection approaches.
2Device complexity
If predefined threshold is used to define neighborhood around nuclei, then processing is simplified, but membrane regions with combined staining patterns are ignored
Solution Approach 1:
The patent employs dynamic region proposal generation instead of fixed predefined neighborhoods. The region proposals are adaptively generated based on image content and staining patterns, allowing the system to capture membrane regions with combined staining while maintaining manageable complexity through automated region generation.
Solution Approach 2:
The patent changes the parameter space by using machine learning models that can handle multiple staining patterns simultaneously. Instead of fixed threshold parameters, the system learns optimal parameters from training data, enabling detection of complex combined staining patterns without manual parameter adjustment.
3Measurement precision
If color deconvolution is applied to separate stain signals, then staining quantification is improved, but processing time and computational complexity increase
Solution Approach 1:
The patent performs color deconvolution as a preliminary step before main classification, pre-processing the image to separate stain signals. This preliminary action enables subsequent faster classification by providing pre-separated color channels, thus reducing overall processing time while maintaining quantification accuracy.
Solution Approach 2:
The patent maintains continuous useful action by integrating color deconvolution results throughout the classification pipeline. The separated stain signals are continuously utilized in feature extraction and classification stages, maximizing the value of the initial deconvolution effort and reducing redundant processing.
Data Source
AI summary
An image analysis system and method classify cells in a tissue image. The system and method may extract at least one image feature characterizing an object in the tissue image. Based on the extracted image feature, cells may be classified according to at least one predefined membrane pattern. For each classified cell, a class label that identifies a class to which the classified cell belongs may be outputted.