OoC Cell Image Classification for Early Differentiation Assessment
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current Organ-on-a-Chip (OoC) techniques require cells and tissues to reach a certain culture age for observation, necessitating time-consuming and error-prone manual analysis, which is costly and inefficient.
Innovation Solution
A system and method utilizing an image capturing module and analysis module, including a convolutional neural network (CNN), to analyze cell and tissue images from OoC, enabling early identification of cell and tissue function and quality through image feature extraction and classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual observation and analysis methods are used to determine cell and tissue function and quality, then analysis accuracy can be maintained, but the process becomes time-consuming and error-prone
Solution Approach 1:
The patent replaces manual mechanical observation and analysis with an automated image analysis system comprising an image capturing module and an analysis module. The analysis module uses automated image processing techniques to extract features and classify cell/tissue images, substituting human operators with a computational system that operates faster and without fatigue, thereby reducing culturing time while maintaining or improving analysis accuracy
Solution Approach 2:
The patent creates digital copies of cell and tissue images through the image capturing module, which captures images at various culturing stages. These digital copies are then processed by the analysis module to determine cell/tissue function and quality without requiring physical manipulation or prolonged culturing, thus reducing the time loss associated with manual observation
2Measurement precision
If cells and tissues are cultured to sufficient age for expression of biology status and reaction to staining agents, then accurate function and quality assessment can be achieved, but the culturing process becomes time-consuming and costly
Solution Approach 1:
The patent performs preliminary image capture and analysis at initial stages of culturing, before cells and tissues reach full maturity. The analysis module is trained to recognize and extract features that indicate future function and quality, allowing assessment to be made in advance. This preliminary action enables earlier decision-making and reduces the time and cost associated with prolonged culturing while maintaining assessment accuracy
Solution Approach 2:
The patent changes the parameter being measured from requiring full cell/tissue maturity to detecting early-stage indicators of function and quality. The analysis module is designed to extract relevant features from images taken at various stages, transforming the measurement approach to enable assessment at earlier time points, thereby improving culturing productivity without sacrificing measurement precision
3Measurement precision
If manual analysis by research personnel is used, then accurate observation can be achieved, but human error increases and operational complexity increases
Solution Approach 1:
The patent replaces manual observation by research personnel with an automated analysis module that performs image processing and classification. This substitution eliminates human error associated with manual analysis while maintaining observation accuracy through consistent, repeatable automated processes. The system handles the complexity of analysis internally, simplifying the operational process for users
Solution Approach 2:
The analysis module performs self-service by automatically capturing images, extracting features, and classifying cell/tissue images without requiring manual intervention. The system trains itself on datasets and autonomously makes assessments, reducing the operational burden on research personnel while maintaining high accuracy through automated, consistent analysis processes
Data Source
AI summary
A system and method for analyzing image of cell and/or tissue are provided. The system may carry an organ-on-a-chip having the cell and/or the tissue, and may have an image capturing module and an analysis module. The image capturing module may be used to capture the image of the cell and/or the tissue from the organ-on-a-chip. The analysis module may be used to extract image feature from the image of the cell and/or the tissue, and label a classification of the cell and/or the tissue according to the image feature.