Candidate Cell Image Analysis Using Overlap Thresholds
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Solution Overview
Problem
The detection and enumeration of circulating tumor cells (CTCs) in biological fluids is challenging due to their low concentration and the high background of hematopoietic cells, making accurate identification and enumeration difficult using existing methods.
Innovation Solution
A method involving staining cell nuclei and cytoskeletal features with bio-conjugated dyes of different colors, followed by image analysis using a computer program to identify and classify candidate cells based on spatial and intensity thresholds, reducing the need for manual review.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual identification and enumeration of CTCs is performed, then detection accuracy can be maintained through human expertise, but the workload and time consumption increase significantly
Solution Approach 1:
The system enables automated self-service detection by implementing computer vision algorithms that automatically identify, classify, and enumerate CTCs in digital pathology images. The algorithm processes images independently without requiring manual review, thereby reducing time consumption while maintaining detection accuracy through programmed classification criteria.
Solution Approach 2:
The patent replaces the mechanical manual review process with an automated computer vision system. The algorithm substitutes human operators by implementing digital image processing techniques including color thresholding, connected component analysis, and classification rules to automatically detect and enumerate CTCs, thereby eliminating the time-consuming manual workload.
2Productivity
If automated image analysis is implemented to reduce manual workload, then processing speed increases, but detection precision may deteriorate due to algorithm limitations
Solution Approach 1:
The detection process is segmented into distinct automated stages: image preprocessing, candidate region identification through color thresholding, connected component analysis for cell boundary detection, and classification based on morphological features. This segmentation allows each stage to be optimized independently, maintaining high processing speed while ensuring detection precision through systematic feature evaluation.
Solution Approach 2:
The algorithm employs adjustable parameter thresholds for color intensity, cell size, shape factors, and classification criteria. These parameters can be tuned to match specific CTC characteristics and staining protocols, enabling the system to adapt to different sample types while maintaining both high processing speed and detection precision through optimized parameter settings.
3Adaptability or versatility
If multiple staining protocols are supported to handle diverse CTC characteristics, then detection versatility improves, but system complexity increases
Solution Approach 1:
The system implements a universal image analysis framework that can handle multiple staining protocols through configurable parameter sets. The core algorithm remains consistent while allowing customization of color thresholds, feature weights, and classification criteria to accommodate different staining methods, thereby achieving detection versatility without proportionally increasing system complexity.
Solution Approach 2:
The system manages complexity by using parameter-based configuration rather than separate algorithms for each staining protocol. Users can adjust color space thresholds, intensity ranges, and morphological parameters to match different staining characteristics, enabling the same core detection engine to handle diverse protocols efficiently without requiring multiple complex systems.
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
Automated identification and enumeration of CTCs significantly reduces the workload for human operators, enhances detection efficiency, and aids in predicting disease progression and patient prognosis.
Implementation Method 1
staining cell nuclei in the biological fluid specimen with a first bio-conjugated dye having a first color and configured to bind nucleic acids in the cell nuclei of the target cells and staining cytoskeletal cell features in the biological fluid specimen with a second bio-conjugated dye having a second color and configured to bind to cytoskeletal cell features of the target cells
Data Source
AI summary
A method for identifying candidate target cells within a biological fluid specimen includes a digital image of the biological fluid specimen with the digital image having a plurality of color channels, identifying first connected regions of pixels of a minimum first intensity in a first channel, identifying second connected regions of pixels of a minimum second intensity in a second channel, and determining first connected regions and second connected regions that spatially overlap. For a pair of a first connected region and a second connected region that spatially overlap, whether the second connected region overlaps the first connected region by a threshold amount is determined, and if the second connected region overlaps the first connected region by the threshold amount then the portion of the image corresponding to the overlap is continued to be treated as a candidate for classification.