Automated Image Analysis for Progenitor Cell Colony Characterization
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Solution Overview
Problem
Current methods for analyzing progenitor cells, such as connective tissue progenitor cells, are time-consuming, labor-intensive, and subjective, especially when assessing colony morphology and performance, as they rely on manual counting and subcloning strategies that can alter the cell characteristics.
Innovation Solution
A computer-implemented method and system for image analysis that provides a montaged image of biological samples, allowing for quantitative analysis and clustering of image objects based on predefined criteria, enabling efficient characterization of cells and colonies, including progenitor cells, with automated segmentation and clustering processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual counting and subcloning strategies are used to analyze progenitor cell colonies, then detailed information about colony morphology can be obtained, but the process becomes highly time-consuming and labor-intensive
Solution Approach 1:
The patent replaces manual mechanical counting and subcloning procedures with an automated image analysis system that uses computer algorithms to identify, segment, and characterize cell colonies. The system captures images of colonies, automatically distinguishes them from background, and extracts morphological features without human intervention, thereby eliminating the time-consuming nature of manual analysis while maintaining measurement precision.
Solution Approach 2:
The patent creates digital copies of colony images and processes these copies through automated algorithms. By working with image data rather than physical samples, the system can analyze multiple colonies simultaneously and extract morphological information without the need for time-consuming manual examination or subcloning procedures.
2Measurement precision
If subcloning strategies are employed to assess progenitor cell performance, then detailed performance information can be obtained, but the process is costly and alters the characteristics of the subcloned population
Solution Approach 1:
The patent replaces the biological subcloning process with an optical imaging and computational analysis system. Instead of physically separating and culturing subclones that are subject to selective pressures, the system uses image capture and automated algorithms to assess colony morphology and infer progenitor cell performance, completely avoiding the harmful selective pressures inherent in subcloning.
Solution Approach 2:
The patent creates digital replicas of colony images and performs all analysis on these copies. This allows for repeated, non-invasive assessment of colony characteristics without physically manipulating the cells or subjecting them to the selective pressures that occur during subcloning, thereby preserving the original cell characteristics while still obtaining detailed performance information.
3Productivity
If automated colony counting methods are used, then time consumption is reduced, but detailed colony morphology information cannot be characterized
Solution Approach 1:
The patent applies image segmentation techniques to divide the image into distinct regions: background, individual colonies, and sub-features within colonies. This segmentation enables the automated system to not only count colonies efficiently but also to extract detailed morphological information from each segmented region, including size, shape, texture, and internal structure characteristics.
Solution Approach 2:
The patent transitions from simple numerical counting to multi-dimensional morphological analysis by extracting multiple features from each colony image. Instead of merely counting objects, the system analyzes dimensions such as area, perimeter, circularity, texture patterns, and intensity distributions, thereby obtaining detailed morphology information while maintaining automated processing speed.
4Measurement precision
If conventional light microscopy with filters is used to analyze stained tissue sections, then optical density measurements can be obtained, but the process requires manual identification and classification by technicians
Solution Approach 1:
The patent replaces the manual process of identifying and classifying stained tissue sections with an automated image analysis system. The system uses computer algorithms to automatically detect stained regions, calculate optical density measurements, and classify features without requiring technician intervention, thereby maintaining measurement precision while dramatically simplifying operation.
Solution Approach 2:
The patent implements a self-service automated analysis system that performs all identification, measurement, and classification tasks without human assistance. The system automatically processes images, applies staining protocols digitally, calculates optical density values, and generates results, enabling the analysis to serve itself without requiring skilled technicians to perform manual identification and classification.
Data Source
AI summary
Systems and methods are described for performing image analysis. A computer-implemented method for analyzing images may include quantitatively analyzing image data to identify image objects relative to a background portion of the image according to predefined object criteria, the image data including a plurality of image objects that represent objects in a sample distributed across a substrate. The identified image objects are further clustered into groups or colonies of the identified image objects according to predetermined clustering criteria.