Context-Guided Histological Segmentation in Digital Pathology
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Solution Overview
Problem
Current digital pathology workflows for disease diagnosis and grading are subjective, time-consuming, and prone to errors due to manual interpretation of histopathological structures in tissue images.
Innovation Solution
A method and system for scalable and high precision context-guided segmentation of histological structures, including ducts/glands, clusters of ducts/glands, and individual nuclei, using multi-parameter cellular and sub-cellular imaging data. This involves breaking down image data into superpixels, assigning probabilities using pre-trained machine learning algorithms, and refining boundaries using a contour algorithm.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual interpretation of histopathological structures is used, then diagnostic decisions can be made, but the process is subjective, time-consuming, and error-prone
Solution Approach 1:
The patent replaces the manual mechanical interpretation process with an automated computer-based system that uses image processing algorithms and machine learning models to segment and analyze histopathological structures, eliminating human subjectivity and time constraints while maintaining or improving diagnostic precision
Solution Approach 2:
The system enables self-service automation where the computer automatically performs segmentation, boundary detection, and classification of histological structures without requiring manual pathologist intervention for each analysis, thereby increasing productivity while maintaining consistent measurement precision through algorithmic objectivity
2Measurement precision
If automated segmentation is implemented, then diagnostic consistency is improved, but system complexity increases
Solution Approach 1:
The patent divides the complex task of histopathological analysis into distinct segmentation stages (coarse segmentation, boundary refinement, structure classification) that can be processed separately and independently, reducing overall system complexity while maintaining high segmentation accuracy through specialized algorithms for each stage
Solution Approach 2:
The system performs preliminary preprocessing steps including image normalization, stain separation, and feature extraction before main segmentation, which simplifies the subsequent analysis by preparing standardized input data and reducing the complexity of the core segmentation algorithms
3Measurement precision
If multi-parameter imaging data is processed, then segmentation precision is enhanced, but computational requirements increase
Solution Approach 1:
The patent processes multiparameter imaging data by segmenting it into separate parameter channels (e.g., different stain channels, focal planes) that can be analyzed independently and then integrated, reducing computational energy requirements compared to processing all parameters simultaneously while maintaining enhanced segmentation precision
Solution Approach 2:
The system applies partial processing by focusing computational resources on critical regions of interest and parameters most relevant to specific diagnostic tasks, rather than uniformly processing all multi-parameter data, thereby reducing overall computational energy while maintaining precision where it matters most
Data Source
AI summary
A method (and system) of segmenting one or more histological structures in a tissue image represented by multi-parameter cellular and sub-cellular imaging data includes receiving coarsest level image data for the tissue image, wherein the coarsest level image data corresponds to a coarsest level of a multiscale representation of first data corresponding to the multi-parameter cellular and sub-cellular imaging data. The method further includes breaking the coarsest level image data into a plurality of non-overlapping superpixels, assigning each superpixel a probability of belonging to the one or more histological structures using a number of pre-trained machine learning algorithms to create a probability map, extracting an estimate of a boundary for the one or more histological structures by applying a contour algorithm to the probability map, and using the estimate of the boundary to generate a refined boundary for the one or more histological structures.