Graph-Based 3D Medical Image Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current medical image segmentation methods are limited in their ability to perform optimal, automated analysis of 3-D and 4-D medical images, often requiring substantial human supervision and interaction due to challenges in achieving globally optimal solutions and accurately delineating object boundaries, especially in the presence of complex or interacting surfaces.
Innovation Solution
The development of graph-based segmentation methods that build a weighted directed graph for each surface, optimize a piecewise-constant cost functional, and use minimum-cost closed sets to detect surfaces, allowing for simultaneous detection of multiple interacting surfaces while enforcing smoothness and spatial separation constraints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional segmentation methods are used, then object localization can be achieved, but accurate border/surface delineation fails
Solution Approach 1:
The patent divides the segmentation process into two distinct stages: localization stage and delineation stage. The localization stage identifies the object of interest, while the delineation stage accurately delineates the object's borders or surfaces. This two-stage segmentation approach allows each stage to be optimized for its specific purpose, resolving the contradiction between localization accuracy and border delineation accuracy.
Solution Approach 2:
The patent transitions from analyzing sequences of 2-D image slices to performing segmentation directly in 3-D space. By moving to 3-D space, the method gains contextual slice-to-slice information and produces consistent segmentation results with object surfaces instead of individual contours, thereby improving border/surface delineation accuracy while maintaining localization capability.
2Device complexity
If 2-D image slice analysis is used, then processing simplicity is maintained, but contextual slice-to-slice information is lost
Solution Approach 1:
The patent moves from 2-D slice-by-slice analysis to 3-D volumetric analysis. This dimensional transition enables the method to utilize contextual information across multiple slices simultaneously, producing consistent segmentation results throughout the volume while maintaining computational tractability through efficient 3-D algorithms.
Solution Approach 2:
The patent combines multiple 2-D slices into a unified 3-D volumetric representation. By merging the information from all slices into a single 3-D space, the method preserves contextual relationships between slices and enables consistent segmentation across the entire volume, eliminating the information loss inherent in independent 2-D analysis.
3Productivity
If automated segmentation tools are used, then analysis speed increases, but user interaction requirements increase excessively
Solution Approach 1:
The patent develops fully automated segmentation methods that perform both localization and delineation without requiring excessive user interaction. The graph-based algorithms automatically identify objects and delineate their boundaries in 3-D space, making the system self-sufficient and reducing the need for manual intervention while maintaining high analysis speed and accuracy.
Solution Approach 2:
The patent employs graph-based algorithms with optimized cost functions and parameters that enable fully automated operation. By carefully selecting and tuning the parameters of the graph construction and search algorithms, the system achieves robust automated segmentation performance without requiring excessive user input or interaction.
4Measurement precision
If existing segmentation tools are used, then some quantitative analysis is possible, but reliability in presence of disease fails
Solution Approach 1:
The patent develops segmentation methods with optimized parameters and cost functions specifically designed to handle diseased conditions. The graph-based algorithms incorporate disease-aware parameters that maintain segmentation reliability even when anatomical structures are altered by pathology, ensuring accurate quantitative analysis in clinically relevant scenarios.
Solution Approach 2:
The two-stage segmentation approach (localization followed by delineation) enhances reliability in diseased conditions by allowing the localization stage to identify the object of interest robustly, and the delineation stage to accurately define its boundaries even when shape or intensity characteristics are altered by disease.
Data Source
AI summary
A system and methods for the efficient segmentation of globally optimal surfaces representing object boundaries in volumetric datasets is provided. An optical surface detection system and methods are provided that are capable of simultaneously detecting multiple interacting surfaces in which the optimality is controlled by the cost functions designed for individual surfaces and by several geometric constraints defining the surface smoothness and interrelations. The graph search applications use objective functions that incorporate non-uniform cost terms such as “on-surface” costs as well as “in-region” costs.