Blood Vessel Segmentation Using Multi-Marker Fluorescence Imaging
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Solution Overview
Problem
Current methods for characterizing and quantifying blood vessels in cancer research, such as immunohistochemical staining, face limitations due to the complex geometry and heterogeneity of tumor vasculature, leading to inaccurate segmentation and quantification of vessel density, which hampers the evaluation of anti-angiogenic treatments.
Innovation Solution
A computer-implemented method for creating a blood vessel map using multi-channel multiplexed fluorescence imaging, combining primary and auxiliary markers like CD31 and CD34 to enhance vessel segmentation through feature extraction, probability mapping, and iterative tracing, thereby improving the accuracy of blood vessel segmentation and quantification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If immunohistochemical staining with single markers (e.g., CD31) is used for vessel segmentation, then the method is simple and fast, but the segmentation accuracy deteriorates due to over-fragmentation and inability to distinguish vessel cells from other cell types
Solution Approach 1:
The patent combines multiple immunohistochemical markers (CD31, CD34, vWF) that target different aspects of vascular structures. By merging the information from these complementary markers through image fusion algorithms, the system achieves more complete and accurate vessel segmentation while maintaining automated processing efficiency.
Solution Approach 2:
The patent creates a composite staining approach using multiple markers with different specificities. Each marker contributes unique information about vascular structures, and their combined signal creates a more robust representation of the complete vascular network, overcoming the limitations of individual markers.
2Measurement precision
If multiple markers are used to improve segmentation accuracy, then the measurement precision improves, but the device complexity and analysis time increase
Solution Approach 1:
The patent divides the complex multi-marker analysis into distinct processing stages: individual marker channel processing, feature extraction from each channel, and subsequent fusion of processed features. This segmented approach manages computational complexity while maintaining the benefits of multi-marker analysis.
Solution Approach 2:
The patent performs preliminary processing of each marker channel independently, including noise reduction, thresholding, and feature extraction, before combining the results. This preliminary action simplifies the subsequent fusion step and reduces the overall computational burden of analyzing multiple markers.
3Ease of manufacture
If CD31 marker is used alone, then the staining procedure is simple, but the vessel count is overestimated due to discontinuous staining and inclusion of non-endothelial cells
Solution Approach 1:
The patent uses auxiliary markers (CD34, vWF) as intermediaries to validate and refine the primary CD31 vessel identification. These intermediary markers help distinguish true endothelial cells from non-endothelial cells that may express CD31, thereby improving counting accuracy.
Solution Approach 2:
The system implements feedback mechanisms where the segmentation results from one marker are used to guide and refine the analysis of other markers. Discrepancies between markers trigger iterative refinement of the segmentation algorithm, improving the accuracy of vessel identification and counting.
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
The method significantly reduces over-fragmentation and underestimation of vessel counts, providing a more complete and continuous segmentation of blood vessels, aligning closer with manual counts and enhancing the analysis of angiogenesis in cancer research.
Implementation Method 1
multiplexed image of a biological tissue fluorescent stained to manifest expression levels of a primary marker and at least one auxiliary marker
Data Source
AI summary
Disclosed are novel computer-implemented methods for creating a blood vessel map of a biological tissue. The methods comprise the steps of, accessing image data corresponding to multi-channel multiplexed image of a fluorescently stained biological tissue manifesting expression levels of a primary marker and at least one auxiliary marker of blood vasculature, and extracting features of blood vessels using the primary marker as an input to create a single channel segmentation of the blood vessels. The method further comprises the steps of extracting features of blood vessels using the auxiliary marker to create auxiliary channels as a second input and apply multi-channel blood vessel enhancement. Blood vessel maps are created using the features and tracing the blood vasculature by iteratively extending the centerlines of the initial segmentation using statistical models and geometric rules.