Biopsy Image Color Range Screening
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Solution Overview
Problem
Current methods for diagnosing prostatic adenocarcinoma and colon cancer through histological examination of biopsy samples are time-consuming and labor-intensive, requiring extensive manual analysis by medically trained personnel, with over 50% of samples being benign and thus not needing further assessment.
Innovation Solution
A method utilizing computer analysis of digital images from immunohistochemically stained biopsies to rapidly screen and categorize samples into normal and malignant categories by defining a colour range specific to each category, reducing the need for manual inspection and streamlining the diagnostic process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual histological examination of biopsy samples is performed by pathologists, then diagnostic accuracy is maintained, but the process becomes time-consuming and labor-intensive
Solution Approach 1:
A computer-based image analysis system acts as an intermediary between the biopsy sample and the pathologist. The system performs preliminary screening by analyzing digital images of stained biopsy samples, automatically identifying and categorizing samples as normal or malignant. This intermediary step filters out clearly benign cases before human review, maintaining diagnostic accuracy while significantly reducing the time pathologists spend on routine examinations.
Solution Approach 2:
The patent replaces the mechanical system of manual microscopic examination with an automated computer vision system. Digital images of immunohistochemically stained biopsy samples are processed through algorithms that detect cellular patterns and staining characteristics. This substitution eliminates the time-consuming manual review of every sample while preserving diagnostic quality through the use of standardized image analysis protocols.
2Reliability
If all biopsy samples are examined manually by pathologists, then no samples are missed, but the workload for medically trained personnel increases significantly
Solution Approach 1:
The patent segments the biopsy sample population into distinct categories using computer analysis. Samples are divided into normal, benign, and malignant groups based on image characteristics. This segmentation allows the system to handle different types of samples appropriately, ensuring reliable identification of malignant cases while directing only the necessary samples for pathologist review, thereby improving both reliability and productivity.
Solution Approach 2:
The computer-based system performs self-service by automatically analyzing and categorizing biopsy samples without requiring pathologist intervention for every case. The system independently evaluates digital images, applies classification algorithms, and generates preliminary diagnoses. This self-service capability maintains reliable sample identification while freeing pathologists from routine tasks, significantly increasing overall workflow productivity.
3Productivity
If computer analysis is used to screen biopsy samples, then the workload for pathologists is reduced, but the complexity of the diagnostic system increases
Solution Approach 1:
The patent uses copying by creating digital replicas of biopsy samples through high-quality imaging. These digital copies are then analyzed by computer algorithms without affecting the original physical samples. The copying process enables automated analysis while maintaining the integrity of the diagnostic workflow, improving screening efficiency without requiring complex physical manipulation or additional equipment beyond standard imaging devices.
Data Source
AI summary
The present invention relates to a method for defining a colour range specific for a chosen category of samples, for use when identifying presence of cells of different categories in a tissue or cell sample. Further, methods for using the obtained colour range in selection of sample images comprising cells of a first category is also disclosed.