Stem Cell Detection in Cell Images via Segmentation and Parallel Processing
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Solution Overview
Problem
Accurately determining the distribution of stem cells in cell images is challenging due to inconsistencies in size and shape, as well as overlap with other cells and impurities, leading to low accuracy.
Innovation Solution
A method involving an electronic device that segments cell images into sub-images, normalizes and gamma corrects them, and uses a stem cell detection model trained with residual convolutional and feature pyramid networks to detect stem cells in parallel, improving accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional detection methods are used to identify stem cells in cell images, then the detection process is simple, but the accuracy is low due to inconsistent size and shape of stem cells and overlap with other cells and impurities
Solution Approach 1:
The patent divides the cell image into multiple sub-images through segmentation, allowing parallel processing of different regions. This segmentation approach enables the detection system to handle complex images more efficiently while maintaining high accuracy in identifying stem cells amidst varying sizes, shapes, and overlapping cells.
Solution Approach 2:
The patent transforms the detection problem from a single-image analysis to a multi-dimensional approach by dividing images into sub-images and processing them in parallel across multiple computational dimensions. This dimensional transformation enables simultaneous processing of multiple image regions, improving both accuracy and efficiency.
2Measurement precision
If detailed analysis of each cell image is performed to accurately determine stem cell distribution, then the detection accuracy improves, but the processing time increases
Solution Approach 1:
The patent segments cell images into multiple sub-images that can be processed independently and in parallel. This segmentation allows the system to perform detailed analysis of each sub-image simultaneously, maintaining high accuracy in determining stem cell distribution while significantly reducing overall processing time through parallel computation.
Solution Approach 2:
The patent applies preprocessing operations (normalization and gamma correction) to sub-images before detailed analysis. This preliminary action prepares the data in advance, enabling faster and more accurate detection of stem cells in the subsequent processing stages without compromising detection accuracy.
3Measurement precision
If noise is present in cell images, then the image processing becomes more complex, but the detection accuracy decreases due to interference from noise
Solution Approach 1:
The patent applies normalization and gamma correction as preliminary processing steps to reduce noise in cell images before detailed analysis. This preliminary noise reduction prepares the images for more accurate stem cell detection while maintaining relatively simple processing procedures, as the noise is addressed early in the workflow.
Data Source
AI summary
A method of determining a distribution of stem cells in a cell image, an electronic device and a storage medium are disclosed. The method acquires a cell image and segments the cell image and obtaining a plurality of sub-images. The plurality of sub-images is inputted into a stem cell detection model to detect to obtain a number of stem cells in each sub-image. A position of each sub-image in the cell image is determined. A distribution of the stem cells in the cell image is output, according to the number of stem cells in each sub-image and the position of each sub-image in the cell image. The present disclosure an accuracy of the distribution of stem cells in the cell image.