Automated Microorganism Colony Counting and Classification
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Solution Overview
Problem
Conventional methods for counting and classifying microorganisms in growth media are prone to errors, especially with large numbers of colonies, and require skilled technicians to handle confluent growth and distinguish between bacterial and fungal colonies, leading to inefficiencies and increased costs in laboratories.
Innovation Solution
An automated system using a colony-counting device with convolutional neural networks and mask residual-CNN models for detecting, segregating, and classifying microorganisms, which includes image processing and deep learning techniques to accurately count and classify individual colonies into species types.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual counting by trained technicians is used, then classification accuracy between bacterial and fungal colonies is improved, but productivity decreases and operational costs increase
Solution Approach 1:
The patent replaces the manual mechanical counting process with an automated optical system using a camera to capture images of colonies, followed by digital image processing and machine learning algorithms to automatically count and classify colonies, eliminating the need for manual technician intervention while maintaining high accuracy
Solution Approach 2:
The system enables self-service automation where the computer automatically performs colony detection, counting, and classification without requiring trained technicians to manually examine and categorize each colony, allowing the system to serve itself through automated image analysis pipelines
2Ease of operation
If manual counting by technicians is used, then handling of confluent growth and overlapping colonies is improved, but measurement precision decreases due to human errors
Solution Approach 1:
The patent applies image processing techniques to segment and separate overlapping or confluent colonies in captured images, using edge detection, thresholding, and contour analysis to identify individual colony boundaries even when colonies appear merged, enabling accurate counting of each distinct colony unit
Solution Approach 2:
The system introduces an intermediary image processing layer between the raw colony image and the final count, using algorithms to detect, separate, and identify individual colonies within complex overlapping patterns, acting as a mediator that translates visual data into accurate quantitative measurements
3Measurement precision
If a huge volume of dishes is used to accommodate large colony counts, then measurement precision is improved, but device complexity and operational costs increase
Solution Approach 1:
The patent creates a digital copy of the physical colony distribution across multiple dishes by capturing images and processing them through the automated system, allowing virtual analysis and counting of large numbers of colonies without requiring proportional increases in physical laboratory infrastructure or manual processing capacity
Data Source
AI summary
Methods and systems for automated counting and classifying microorganisms. A method disclosed herein includes receiving and analyzing quality of at least one input media of at least one incubated dish used for growth of the colonies of the microorganisms. The method further includes detecting the colonies of the microorganisms in a growth medium disposed on the dish if the received at least one media is a good quality media, wherein the detected colonies include at least one of individual colonies and grouped colonies. The method further includes segregating the grouped colonies into the individual colonies. The method further includes classifying the individual colonies into at least one species of the microorganisms. The method further includes counting the colonies of each species.