AI Cell Labeling System for Microscopy Automation
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Solution Overview
Problem
Manual cell labeling in biomedical samples is labor-intensive, time-consuming, and prone to inaccuracies, especially when dealing with large numbers of images, which affects both efficiency and ocular health of operators and the accuracy of cellular analysis.
Innovation Solution
An AI-assisted automatic labeling system that uses pre-labeled images to train a basic model, verifies and modifies it using verification data to improve accuracy, and assigns similarity scores to determine the need for retraining, thereby automating the labeling process and reducing manual effort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual cell labeling is performed on thousands or tens of thousands of cells in multiple images, then complete cell labeling can be achieved, but it consumes much manpower and time and impairs ocular health
Solution Approach 1:
The patent replaces the manual mechanical labeling process with an automated computer-based system. The system captures microscope images, processes them through software algorithms, and automatically identifies and labels cells, substituting human manual operation with automated computational methods.
Solution Approach 2:
The system enables self-service by allowing the computer to automatically perform cell labeling without continuous human intervention. Once the system is set up and trained, it can independently process and label cells in multiple images, reducing reliance on manual labor.
2Measurement precision
If manual cell labeling is performed on numerous images, then all cells can be labeled, but it consumes much manpower and reduces productivity
Solution Approach 1:
The patent replaces the manual mechanical labeling process with an automated computer-based system. The system captures microscope images, processes them through software algorithms, and automatically identifies and labels cells, substituting human manual operation with automated computational methods.
Solution Approach 2:
The system enables continuous automated processing of multiple images without interruption. The computer can sequentially process numerous images and label cells continuously, maintaining high productivity without the breaks and fatigue inherent in manual labeling.
3Productivity
If manual cell labeling is performed by operators, then cell labeling can be completed, but uniformity and accuracy of outputs are hard to achieve
Solution Approach 1:
The patent replaces the manual mechanical labeling process with an automated computer-based system. The system captures microscope images, processes them through software algorithms, and automatically identifies and labels cells, substituting human manual operation with automated computational methods.
Solution Approach 2:
The system incorporates feedback mechanisms where the computer analyzes image data, applies labeling algorithms, and can refine its performance based on results. This feedback loop ensures consistent and uniform labeling outputs across multiple images, improving reliability compared to manual labeling.
Data Source
AI summary
An AI-assisted automatic labeling system and a method thereof are disclosed. The method comprises steps: selecting images from microscopic images as candidate images, using a pre-labeling module to automatically label cells in the candidate images, and dividing the labeled images into training data and verification data; using a training module and the training data to train a basic model; using a verification module to verify and modify the basic model, wherein the verification module respectively verifies at least one cell area and at least one background area of the verification data to converge the basic model and form an automatic labeling model; using the automatic labeling model to automatically label cells in redundant images of the microscopic images. The basic model trained by the present invention can use few labeled images to perform regressive training and verification and then automatically labels the redundant images accurately and efficiently.