Tissue Section Thickness Estimation Using Focus-Difference Imaging
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Solution Overview
Problem
The variability in tissue section thickness during the microtomy process in histopathology leads to inconsistent image quality, hindering accurate pathological diagnosis and the development of AI applications, as hospitals lack standardized and affordable equipment to measure and standardize section thickness.
Innovation Solution
A tissue section thickness estimation device that generates a difference image between shallow and deep depth of focus microscopic images, utilizing machine learning to estimate section thickness, and optionally includes a model generator for supervised learning using teacher data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual microtomy is performed by medical technologists to achieve tissue section thickness of 4 μm, then the tissue sections can be prepared for pathological diagnosis, but the thickness variation cannot be completely eliminated and standardization cannot be achieved
Solution Approach 1:
The patent replaces manual mechanical microtomy operations with an automated image processing system. The system captures microscopic images of tissue sections and uses computer algorithms to calculate thickness based on optical path differences, eliminating the need for manual thickness control while achieving standardized measurement and evaluation.
Solution Approach 2:
The patent introduces an intermediary computational model that acts as a mediator between the physical tissue section and its thickness measurement. The model uses image intensity values and optical path calculations to indirectly determine thickness without direct physical measurement, bridging the gap between visual inspection and quantitative analysis.
2Manufacturing precision
If expensive fully automated tissue sectioning and staining devices are introduced to standardize tissue sections, then mechanized standardized process can be achieved, but the cost becomes tens of millions of yen making it difficult to introduce widely in general hospitals
Solution Approach 1:
The patent replaces expensive, complex automated microtomy equipment with a cost-effective software-based solution that runs on ordinary computers and microscopes. The computational thickness estimation method uses readily available image processing tools and algorithms, eliminating the need for costly specialized hardware while achieving standardized thickness evaluation.
Solution Approach 2:
The patent creates a virtual copy of the physical tissue section through digital imaging. Instead of physically measuring or mechanically controlling thickness, the system captures an optical image and generates a digital representation from which thickness can be calculated using image intensity analysis and optical path modeling.
3Measurement precision
If confocal laser microscopy or white interference microscopy is used to measure tissue section thickness, then the thickness can be accurately determined, but these devices are not usually provided by general hospitals
Solution Approach 1:
The patent makes thickness measurement capability universal by implementing it in ordinary optical microscopes through software processing. Instead of requiring specialized confocal or interference microscopes, the system uses standard microscope image capture combined with computational algorithms to achieve thickness estimation, making the functionality available in general hospital settings.
Solution Approach 2:
The patent replaces complex optical measurement systems (confocal microscopy, white interference microscopy) with a computational approach using ordinary microscopes. The system substitutes sophisticated optical hardware with software-based image analysis that calculates thickness from standard optical images through intensity-based modeling.
4Ease of operation
If tissue sections are cut at 8 μm thickness, then the sectioning process is simpler, but the cells overlap each other and the image becomes out of focus making accurate pathological diagnosis difficult
Solution Approach 1:
The patent implements a feedback mechanism where the system measures the actual thickness of each tissue section using image analysis, then provides this information back to evaluate whether the section is suitable for diagnosis. The thickness calculation based on optical path differences gives quantitative feedback that allows assessment of section quality without requiring perfect manual thickness control.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate estimation of tissue section thickness from ordinary optical microscopic images, enhancing standardization and facilitating AI-assisted pathological diagnosis without the need for expensive equipment.
Implementation Method 1
an optical unit that generates an optical image of a tissue section
Implementation Method 2
a condenser that adjusts a depth of focus of the optical image
Data Source
AI summary
A tissue section thickness estimation device comprises a difference image generator that generates a difference image between a microscopic image of a tissue section taken under conditions of shallow depth of focus and a microscopic image of the tissue section taken under conditions of deep depth of focus, and an estimation unit that estimates the thickness of the tissue section from the difference image.