Antinuclear Antibody Fluorescence Pattern Detection via Image Segmentation
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Solution Overview
Problem
Current methods for detecting antinuclear antibody fluorescence patterns in human epithelioma cells require processing entire fluorescence images, which is computationally intensive and may lead to unreliable pattern detection due to the high degree of freedom in abstract image information.
Innovation Solution
The method involves segmenting the total image into sub-images focused on mitotic cells, particularly those in the metaphase stage, and using a convolutional neural network to detect specific fluorescence patterns within these sub-images, thereby reducing the computational load and improving pattern detection accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If entire fluorescence images are processed to detect antinuclear antibody fluorescence patterns, then comprehensive pattern detection is achieved, but computational intensity increases and detection reliability decreases
Solution Approach 1:
The patent segments the total fluorescence image into multiple sub-images, each containing a limited number of cells (e.g., 5-20 cells). This segmentation reduces the computational complexity by processing smaller image portions independently, while maintaining detection reliability through systematic evaluation of all segments.
Solution Approach 2:
The patent extracts and focuses on specific regions of interest (mitotic cells in metaphase stage) from the total image. By taking out only the relevant cellular regions for pattern analysis, the method reduces computational load while improving detection reliability through concentrated analysis on the most informative areas.
2Measurement precision
If entire fluorescence images are processed to detect fluorescence patterns, then all cellular regions are analyzed, but the high degree of freedom in abstract image information leads to unreliable pattern detection
Solution Approach 1:
The patent divides the total image into segments with limited cell counts, making the abstract image information more manageable and less ambiguous. This segmentation reduces the high degree of freedom in the data, improving measurement precision by working with more constrained, interpretable image portions.
Solution Approach 2:
The patent applies different processing qualities to different regions - specifically focusing on mitotic cells in metaphase stage which are most informative for fluorescence pattern detection. This local quality approach concentrates computational resources on the most informative regions, improving overall measurement precision.
3Productivity
If a convolutional neural network processes sub-images of mitotic cells, then computational overhead is reduced, but the scope of analysis is limited to specific cell types
Solution Approach 1:
The patent performs preliminary identification and segmentation of mitotic cells in metaphase stage before applying the convolutional neural network for fluorescence pattern detection. This preliminary action filters and prepares the data, ensuring that only the most informative cell types are analyzed, thereby improving detection efficiency without significantly limiting biological relevance.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach allows for efficient detection of various antinuclear antibody fluorescence patterns by focusing on specific cellular regions, reducing computational overhead, and enhancing the reliability of pattern identification.
Implementation Method 1
Irradiation of the substrate with excitation radiation then yields a fluorescence of said fluorescent dye
Data Source
AI summary
A method and apparatus are provided for detecting respective potential presences of respective different cellular fluorescence pattern types on a biological cellular substrate including human epithelioma cells (HEp cells), wherein the fluorescence pattern types include different antinuclear antibody fluorescence pattern types. A method is also provided for detecting potential presences of different cellular fluorescence pattern types on a biological cellular substrate including human epithelioma cells by means of digital image processing, as well as a computing unit, a data network device, a computer program product and a data carrier signal therefor.