Cell Growth Assessment Using Segmentation Maps for Dense Colonies
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Solution Overview
Problem
The process of assessing cell line growth/proliferation rates is time-consuming and tedious due to the manual review of microscopic images, especially when dealing with dense cell colonies, and requires significant human effort.
Innovation Solution
Automate the visual inspection process using a modified fully convolutional neural network (FCN) to generate down-sampled segmentation maps for cell colony detection, and a fully convolutional regression network (FCRN) for early-stage cell counting, reducing the need for precise pixel labeling and enhancing throughput.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual visual inspection of well images is performed to assess cell growth rates, then measurement precision is improved, but productivity deteriorates due to time-consuming analysis requiring many man-hours
Solution Approach 1:
The patent replaces the manual mechanical visual inspection process with an automated computer-based image analysis system. The system captures images of cell colonies in wells and uses software algorithms to automatically measure colony size, cell density, and growth rates, eliminating the need for manual microscopic examination while maintaining measurement accuracy and significantly increasing throughput capability.
Solution Approach 2:
The automated image analysis system performs self-assessment of cell growth by automatically processing well images, calculating growth metrics, and generating reports without human intervention. The system serves itself by autonomously completing the entire measurement workflow from image capture to data generation, freeing researchers from time-consuming manual analysis tasks.
2Productivity
If automated visual inspection is implemented to increase productivity, then throughput is improved, but measurement precision deteriorates due to difficulties in counting cells in dense colonies and generating training libraries
Solution Approach 1:
The patent applies segmentation by dividing the complex task of cell counting in dense colonies into multiple processing stages: initial colony detection, boundary identification, internal structure analysis, and cell density calculation. This multi-stage segmentation approach allows the system to handle dense colonies effectively by processing different aspects separately and combining results, maintaining precision while enabling automated high-throughput analysis.
Solution Approach 2:
The system employs partial action by focusing measurement efforts on the most informative features of cell colonies rather than attempting to count every individual cell in dense populations. The image analysis algorithm identifies and measures key growth indicators such as colony area, boundary characteristics, and overall cell density, providing sufficient precision for growth rate assessment without requiring exhaustive cell-by-cell counting, thus achieving accurate results efficiently.
3Measurement precision
If precise pixel labeling is performed to improve training library quality, then measurement precision is improved, but loss of time increases due to the tedious labeling process
Solution Approach 1:
The patent applies preliminary action by pre-processing training images to automatically generate initial pixel labels and annotations before formal model training. The system performs preliminary segmentation and feature identification on training data, creating pre-labeled datasets that significantly reduce the manual labeling time required while maintaining high training library quality. This preliminary preparation step accelerates the overall model development process.
Solution Approach 2:
The system uses copying by generating synthetic training images and labels through image processing algorithms and data augmentation techniques. Instead of manually labeling every training sample, the system creates copies and variations of annotated images, automatically generating additional training data with consistent labels. This copying approach expands the training library efficiently while maintaining precision without requiring proportional increases in manual labeling effort.
Data Source
AI summary
A method of facilitating a growth assessment for a cell line includes generating an image of a well that contains a medium that was inoculated with at least one cell of the cell line. The method also includes generating a down-sampled segmentation map comprising pixels that indicate an inferred presence or absence of a cell colony in corresponding portions of the well image. Generating the down-sampled segmentation map includes inputting the well image to a fully convolutional neural network having a plurality of convolutional layers. The method also includes (i) determining, by inputting colony size information (including the down-sampled segmentation map and/or a pixel count derived therefrom) to a cell growth assessment algorithm, a growth classification or score for the cell line, and causing a display of the growth classification or score, and/or (ii) causing a display of the colony size information to facilitate a manual cell growth assessment.