Pathology Thin-Slicing Parameter Generation From Tissue and Block Data
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing automatic section preparation devices for pathological specimens face challenges in flexibly generating necessary parameters for section preparation, as users lack quantitative knowledge of set conditions, and existing online systems lack integrated data for automatic preparation, leading to difficulties in using and upgrading these devices.
Innovation Solution
An automatic thin-cutting device and method that acquires tissue data from a host system, measures paraffin blocks, and generates parameters for section preparation using an input part, data acquisition part, measurement part, storage part, and parameter generation part, allowing for flexible and automated parameter generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If preparation conditions are set using preparation condition tables based on block storage location or cassette ID, then automation of section preparation is improved, but users cannot quantitatively understand or control the parameter values
Solution Approach 1:
The system provides feedback by displaying the actual parameter values (cutting speed, thickness, temperature, time) that are set based on block storage location or cassette ID. This allows users to quantitatively understand the parameters while maintaining automated preparation, resolving the contradiction between automation and parameter transparency.
2Reliability
If online systems are introduced to prevent diagnostic errors, then specimen management reliability is improved, but data required for automatic section preparation is not included
Solution Approach 1:
The system is designed to acquire and utilize multiple types of data (block management data, patient information, examination data) from the existing online system, making the online system serve multiple functions including both specimen management and automatic section preparation control, thereby resolving the contradiction between reliability and adaptability.
3Adaptability or versatility
If users directly input section preparation conditions temporarily, then flexibility in preparation is improved, but it becomes difficult to deal with and understand numerical conditions
Solution Approach 1:
The system introduces an intermediary mechanism (preparation condition tables) that maps block storage locations or cassette IDs to specific parameter values. Users interact with familiar identifiers rather than raw numerical parameters, while the system automatically translates these into precise cutting conditions, resolving the contradiction between flexibility and ease of operation.
4Loss of information
If existing online systems are upgraded to include section preparation data, then data completeness is improved, but cost and time effort increase significantly
Solution Approach 1:
The system extracts and utilizes the data acquisition capability already present in the online system for block management, and supplements it with minimal additional data (block shape, tissue type) collected through simple measurement or user input. This approach obtains complete preparation data without requiring a comprehensive system upgrade, resolving the contradiction between data completeness and upgrade cost.
Data Source
Figure 1~2
Figure 3
AI summary
An acquisition part (10, 11, 12) acquires a plurality of types of data of tissue used to form a pathological section specimen. A storage part (13) stores the data of the tissue. A parameter generation part (14) generates parameters, which are used when preparing the pathological section specimen, based on the data of the tissue acquired by the acquisition part (10, 11, 12). A pathological section specimen preparation part (15) prepares the pathological section specimen using the parameters generated by the parameter generation part (14).