Automated Colony Selection System Preventing Plate Edge Errors
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for selecting colony locations on culture plates are prone to errors, such as selecting colonies too close to each other or features like the edge of the plate, which can lead to incorrect sample retrieval and contamination.
Innovation Solution
A computer-based system with a user interface and processor that determines the location of a selection tool on a culture plate image, identifies potential sources of error, and prevents selection if the tool overlays these areas, using image processing to ensure accurate colony selection and prevent errors like striking the plate edge or picking the wrong organism.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual colony selection is performed on culture plates, then researchers can identify and select specific colonies for analysis, but errors occur when colonies are selected too close to each other or near plate edges, leading to incorrect sample retrieval and contamination
Solution Approach 1:
The patent replaces manual mechanical selection with an automated computer vision system using machine learning models to detect and select colonies. The system captures images of culture plates, processes them through trained neural networks to identify colony locations and characteristics, and automatically selects appropriate colonies for retrieval, eliminating human error in manual selection processes.
Solution Approach 2:
The patent creates a digital copy of the culture plate through image capture and processing. The machine learning system analyzes this digital representation to identify colony positions, characteristics, and optimal selection targets, then uses this information to guide physical sample retrieval, ensuring accurate translation from digital identification to physical collection.
2Measurement precision
If automated image processing is used to identify colonies, then selection accuracy improves, but the system complexity increases due to multiple image captures and processing steps
Solution Approach 1:
The patent employs a multi-functional machine learning system that performs multiple tasks using the same core technology platform. The trained neural network models handle colony detection, classification, location identification, and selection determination within a single integrated system, reducing overall complexity compared to separate specialized systems for each function.
Solution Approach 2:
The system uses self-supervised learning and automated feedback mechanisms where the machine learning models continuously improve through processing actual culture plate images and comparing results with ground truth data. The system automatically adjusts parameters and refines detection algorithms without requiring manual reconfiguration for each new plate type or colony characteristic.
Data Source
AI summary
Systems and methods are provided for selecting colony locations. Selecting colony locations can include determine a location of a selection tool on a culture plate image, determining a location of a potential source of error on the culture plate image, comparing the location of the selection tool to the location of the potential source of error; and determining an error when the location of the selection tool overlays the location of the potential source of error.