Vascular Network Organization via Hough Transform
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Solution Overview
Problem
Existing approaches to predicting tumor response to treatment fail to effectively differentiate between tumors based on vessel arrangement and convolutedness, as they primarily focus within tumor confines and do not consider explicit parenchymal vessel morphology, limiting their ability to predict therapeutic response.
Innovation Solution
The method computes local measures of vessel curvature using the Hough transform to characterize chaotic vasculature associated with tumor-induced angiogenesis, capturing functional attributes of the tumor by defining abnormal vessel arrangements across multiple planes and relative to the tumor core and boundary, and classifies regions of interest based on the VaNgOGH descriptor.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing textural radiomics and deep learning approaches are used to distinguish disease aggressiveness, then prediction capability is improved, but the ability to specifically interpret vessel characteristics is lost
Solution Approach 1:
The patent extracts and isolates vascular network features from the overall tumor imaging data. By separating the vascular component and analyzing it independently through Hough transform, the method recovers lost vessel characteristic information while maintaining prediction capability. The vascular network is extracted as a distinct feature set that can be analyzed separately from general tumor texture features.
Solution Approach 2:
The patent segments the imaging analysis into distinct functional components: tumor region analysis, vascular network analysis, and their interaction. This segmentation allows specific interpretation of vessel characteristics (through Hough transform on vascular masks) while maintaining overall prediction capability through combined features from multiple segmented analysis domains.
2Device complexity
If analysis is limited to tumor confines and immediate peritumoral region, then computational complexity is reduced, but parenchymal vessel morphology is excluded
Solution Approach 1:
The patent extends the analysis from the traditional 2D/3D tumor volume to include the surrounding parenchymal space as an additional dimensional context. By analyzing vascular networks in the peritumoral parenchyma rather than limiting to tumor boundaries, the method captures additional morphological information about vessel architecture in the broader tissue context, effectively adding a spatial dimension to the analysis.
3Measurement precision
If Hough transform is applied to compute local vessel curvature measures, then vessel arrangement characterization is improved, but computational processing time increases
Solution Approach 1:
The patent performs preliminary preprocessing steps including image filtering, vascular mask generation, and region of interest identification before applying the computationally intensive Hough transform. By preparing and pre-processing the data in advance to isolate and enhance only the most relevant vascular structures, the method reduces the computational burden during the actual Hough transform analysis, thereby decreasing overall processing time while maintaining measurement precision.
Data Source
AI summary
Embodiments access a radiological image of tissue having a tumoral volume and a peritumoral volume; define a vasculature associated with the tumoral volume; generate a Cartesian two-dimensional (2D) vessel network representation; compute a first set of localized Hough transforms based on the Cartesian 2D vessel network representation; generate a first aggregated set of peak orientations based on the first set of Hough transforms; generate a spherical 2D vessel network representation; compute a second set of localized Hough transforms based on the spherical 2D vessel network representation; generate a second aggregated set of peak orientations based on the second set of Hough transforms; generate a vascular network organization descriptor based on the aggregated peak orientations; compute a probability that the tissue is a member of a positive class based on the vascular network organization descriptor; classify the ROI based on the probability; and display the classification.