Cell Proportion Estimation Through Nondestructive Image Analysis
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Solution Overview
Problem
Existing methods for determining the proportion of specific cell types in a sample, such as hematopoietic stem cells derived from iPS cells, are inefficient and inaccurate, particularly when the cells are in a nondestructive and non-staining state, leading to incorrect identification of detection targets.
Innovation Solution
An apparatus and method that utilizes image analysis to estimate the proportion of detection targets in a sample by setting extraction conditions based on parameters such as shape and texture, using a combination of image recognition and machine learning to accurately identify and quantify cells without destruction or staining, and includes a preprocessing unit to prepare the sample.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If image analysis is performed on cells in a nondestructive and non-staining state, then sample integrity is maintained, but identification accuracy deteriorates
Solution Approach 1:
The patent applies phase contrast imaging to convert subtle refractive index differences and density variations in unstained cells into visible contrast differences. This allows the imaging system to detect cellular structures and characteristics without requiring chemical stains, thereby maintaining sample integrity while achieving sufficient identification accuracy for distinguishing cell types and states.
Solution Approach 2:
The patent replaces chemical staining methods with optical imaging techniques, specifically phase contrast imaging combined with machine learning analysis. This substitution eliminates the need for destructive chemical treatments while achieving accurate cell identification through computational analysis of optical properties, thus resolving the contradiction between maintaining sample integrity and ensuring identification accuracy.
2Measurement precision
If traditional staining methods are used to identify cell types, then identification accuracy improves, but sample destruction occurs
Solution Approach 1:
The patent replaces chemical staining methods with optical imaging techniques, specifically phase contrast imaging combined with machine learning analysis. This substitution eliminates the need for destructive chemical treatments while achieving accurate cell identification through computational analysis of optical properties, thus resolving the contradiction between maintaining sample integrity and ensuring identification accuracy.
Solution Approach 2:
The patent introduces machine learning algorithms as an intermediary between the optical imaging data and cell identification. The machine learning model processes the phase contrast images to extract subtle features and patterns that are not readily visible to the human eye, enabling accurate cell type classification without requiring chemical stains, thereby maintaining sample integrity while achieving high identification accuracy.
3Measurement precision
If manual cell counting and classification is performed, then identification accuracy can be high, but processing time increases
Solution Approach 1:
The patent replaces manual cell counting and classification with an automated system combining phase contrast imaging and machine learning algorithms. The machine learning model rapidly processes images to identify and classify cells based on their optical properties, achieving accuracy comparable to or exceeding manual methods while dramatically reducing processing time from minutes or hours to seconds.
Solution Approach 2:
The patent implements an automated system where the machine learning model independently performs cell identification and classification without requiring manual intervention. The system self-calibrates using reference samples and automatically processes test samples, eliminating the time-consuming manual counting and classification process while maintaining high identification accuracy through continuous learning and adaptation.
Data Source
AI summary
Provided is an apparatus, including an acquisition unit that acquires an image of a sample containing cells or microorganisms as image-capturing targets; an estimation unit that estimates a proportion of detection targets to image-capturing targets in a sample shown in an image acquired by the acquisition unit, based on a group of an image of a reference sample containing image-capturing targets and a proportion of cells or microorganisms as the detection targets to the image-capturing targets in the reference sample; and an output unit that outputs a proportion estimated by the estimation unit.