Pathologic Image Diagnostic System Priority-Based Block Extraction
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Solution Overview
Problem
Current pathologic image diagnostic systems face inefficiencies in processing and transmission of pathologic tissue images, particularly when dividing images into rectangular blocks for diagnosis, as they often overlook important relationships between distant tissue areas, leading to incomplete or delayed diagnoses during telemedicine and poor overall diagnostic efficiency.
Innovation Solution
A pathologic image diagnostic system that divides captured tissue images into preset areas, sets measurement index values, and assigns significance based on neighboring areas, extracting and transmitting areas with specific tissue proportions along with their measurement and significance values for efficient diagnosis and display.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If a pathologic tissue image is divided into rectangular blocks and diagnosed in mechanical order, then the processing can be systematic and complete, but the diagnosis efficiency is poor and information from different blocks is overlooked
Solution Approach 1:
The pathologic image is divided into multiple blocks, and each block is assigned a priority value based on its diagnostic importance. This segmentation allows the system to process blocks selectively rather than uniformly, improving efficiency while preserving critical information from high-priority blocks.
Solution Approach 2:
The system introduces a priority parameter to characterize the diagnostic importance of each block. By calculating priority values based on tissue characteristics and diagnostic requirements, the system dynamically adjusts processing order and resource allocation, resolving the contradiction between systematic processing and efficiency.
2Reliability
If the entire pathologic tissue image is transmitted for diagnosis, then complete diagnostic information is available, but transmission time and data amount increase
Solution Approach 1:
The system extracts and transmits only the high-priority blocks that contain critical diagnostic information, rather than transmitting the entire image. This extraction approach maintains diagnostic reliability by focusing on essential areas while significantly reducing transmission time and data volume.
Solution Approach 2:
The system performs preliminary analysis to calculate priority values for each block before transmission. This preliminary action identifies which blocks contain critical information, enabling selective transmission that preserves diagnostic accuracy while minimizing transmission requirements.
3Ease of operation
If mechanical procedure and order is used for diagnosing divided image pieces, then the processing method is simple, but the diagnosis efficiency is limited and important relationships are overlooked
Solution Approach 1:
The system replaces static mechanical ordering with dynamic priority-based processing. The processing order is dynamically determined by calculating priority values for each block based on tissue characteristics and diagnostic importance, making the system adaptable while maintaining operational simplicity through automated priority calculation.
Data Source
AI summary
Pathologic tissue images are to be output which allow effective image diagnosis to be performed. There are included an index amount calculating unit that divides a captured pathologic image into a predetermined divisional areas and sets a measurement index value related to a reference of pathologic diagnosis for each divisional area; and a significance calculating module that sets, for each divisional area, a significance related to the pathologic measurement on the basis of both the measurement index value of each divisional area and the measurement index value of a respective divisional area adjacent to that divisional area. The significance calculating module extracts that one of the divisional areas in which the areas for which images of the living tissues have been captured represent a given percentage, and further the significance calculating module associates, with the extracted divisional area, the measurement index value and significance set therefor for transmission.