Automated Cancer Tissue Segmentation via Ensemble Consensus
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Manual analysis of stained tissue sections for cancer detection is labor-intensive and prone to errors due to variability in staining characteristics and nuclei size, making it inefficient for broad clinical applications.
Innovation Solution
An ensemble of segmentation methods is applied to greyscale images of tissue samples, combining individual segmentations using a consensus function to improve accuracy and reject images based on segmentation quality, thereby reducing the time and cost of identifying cancerous tissue.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual analysis of stained tissue sections is used for cancer detection, then diagnostic accuracy can be maintained through expert judgment, but the process becomes labor-intensive and time-consuming
Solution Approach 1:
The patent replaces manual mechanical analysis by pathologists with an automated image processing system that uses digital imaging and computer algorithms to detect and quantify nuclear features, thereby maintaining diagnostic accuracy while significantly improving analysis speed and reducing labor intensity
Solution Approach 2:
The system creates digital copies of tissue sections through microscopy and analyzes these copies using computational methods, allowing repeated measurements and statistical evaluations without requiring additional manual processing, thus improving both speed and reliability
2Productivity
If automatic quantification of stereological parameters is implemented, then analysis speed increases, but segmentation accuracy deteriorates due to variability in staining characteristics and nuclei size
Solution Approach 1:
The patent dynamically adjusts segmentation parameters based on the specific characteristics of each tissue section, including adaptive thresholding values and morphological operation parameters that are optimized for the particular staining pattern and nuclear size distribution observed in each sample, thereby maintaining high segmentation accuracy across varying conditions
Solution Approach 2:
The segmentation algorithm is designed to be dynamic rather than static, continuously adapting to the specific staining characteristics and nuclear morphology of each tissue section through iterative optimization and feedback mechanisms, which enables accurate segmentation despite variability in staining and size
3Loss of time
If a single segmentation method is used, then processing time is reduced, but the ability to handle variability in tissue staining and nuclei size deteriorates
Solution Approach 1:
The patent combines multiple segmentation methods into a unified framework where different algorithms (e.g., thresholding, edge detection, region growing) are integrated and executed in sequence, with each method contributing to the final segmentation result, thereby maintaining speed while improving adaptability to staining variability
Solution Approach 2:
The segmentation system functions as a composite process, integrating multiple algorithmic components that work together synergistically, where the strengths of individual methods compensate for their weaknesses, enabling robust handling of staining variability without significant time penalty
Data Source
AI summary
This invention relates to a system and method for applying an ensemble of segmentations to microscopy images of a tissue sample to determine if the tissue sample is representative of cancerous tissue. The ensemble of segmentations is applied to a plurality of greyscale or color microscopy images to generate a final image level segmentation and a final blob level segmentation. The final image level segmentation and final blob level segmentation are used to calculate a mean nuclear volume to determine if the tissue sample is representative of cancerous tissue.