Automated Tissue Section Handling on Continuous Substrate
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Solution Overview
Problem
Current methods for slicing, staining, and imaging of biological tissues are labor-intensive and often damage tissue sections, leading to incomplete sampling and disruption of spatial relationships, which limits the understanding of molecular and geometric features in tissue pathology.
Innovation Solution
An automated system that adheres tissue sections to a continuous substrate, allowing for sequential and controlled staining, imaging, and analysis, using decision points informed by section images to optimize subsequent steps and improve tissue handling and alignment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual handling of tissue sections is used, then flexibility in decision-making is improved, but tissue damage and labor intensity increase
Solution Approach 1:
The system uses automated image analysis and machine learning algorithms to make decisions about staining and imaging parameters, replacing manual practitioner decisions. The computer automatically determines which sections to stain and what staining protocols to apply based on image features, eliminating the need for human practitioners to physically handle and inspect each section manually.
Solution Approach 2:
The patent replaces manual mechanical handling of tissue sections with an automated system that uses computer-controlled image capture, analysis, and decision-making. The system captures images of sections on the substrate, analyzes them computationally, and automatically determines subsequent processing steps, eliminating the mechanical manipulation that causes tissue damage.
2Manufacturing precision
If uniform application of single-cell techniques to all tissue slices is performed, then completeness of analysis is improved, but time and cost increase significantly
Solution Approach 1:
The system applies different staining and imaging protocols to different sections based on their specific characteristics and diagnostic value. Rather than uniformly processing all sections, the computer analyzes image features and selectively applies resources to sections that meet predetermined criteria for diagnostic importance, optimizing both completeness and efficiency.
Solution Approach 2:
The system performs partial processing by selectively staining and imaging only those sections that meet predetermined diagnostic criteria rather than processing every section. The computer identifies and focuses resources on high-value sections, achieving sufficient diagnostic completeness without the prohibitive time and cost of uniform full-processing.
3Adaptability or versatility
If manual transfer of sections to glass slides is performed, then adaptability in inspection is improved, but section damage and spatial relationship disruption increase
Solution Approach 1:
The patent replaces the mechanical transfer process with digital image capture and computational analysis. Sections remain on the continuous substrate throughout the process, and the computer captures images and analyzes them without requiring physical manipulation or transfer, thereby preserving spatial relationships while maintaining inspection adaptability through digital means.
Solution Approach 2:
The system creates digital copies of tissue sections through image capture rather than requiring physical handling of the actual sections. The computer captures images of sections on the substrate and performs all subsequent analysis on these digital representations, eliminating the need for manual transfer to glass slides while preserving the original spatial relationships on the substrate.
4Productivity
If automation tools are used for section processing, then productivity is improved, but adaptability to unique section traits decreases
Solution Approach 1:
The system incorporates continuous feedback loops where the computer captures images of sections, analyzes their features, and uses this information to automatically adjust staining and imaging parameters for each individual section. The machine learning algorithms learn from image data and adapt processing decisions based on the unique characteristics of each section, combining automation speed with section-specific adaptability.
Solution Approach 2:
The automation system is designed to be dynamic and adaptive rather than rigid. The computer-controlled system can modify staining protocols, imaging parameters, and processing decisions based on real-time image analysis of each section's unique traits. This dynamic capability allows the automated system to respond to section-specific characteristics while maintaining high throughput and productivity.
Data Source
AI summary
An automated tissue section slicing, staining, and imaging system sequentially adheres tissue sections to a continuous substrate such that the staining, imaging, and interpretation of the tissue sections are readily carried out at high speed under machine control.