Semi-Automated Cartilage Segmentation Using Radial Intensity Profiles
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Solution Overview
Problem
Manual segmentation of biological tissues in MR images for assessing cartilage thickness is time-consuming and highly dependent on human expertise, limiting its efficiency and reproducibility for diagnosing and tracking osteoarthritis progression.
Innovation Solution
A semi-automated segmentation system that uses a processor to receive medical image data, identify key points, determine arc points and linear segments, and generate intensity profiles to automatically contour target biological tissues, reducing the need for extensive user input and enhancing accuracy and reproducibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual contouring is used to segment biological tissues, then measurement precision can be achieved, but productivity is extremely low and time-consuming
Solution Approach 1:
The patent segments the segmentation process into distinct phases: user selects key anatomical landmarks (seed points), and the system automatically generates contours by analyzing intensity profiles along radial lines from these points. This divides the complex manual contouring task into automated intensity analysis and minimal user input, resolving the contradiction between precision and productivity.
Solution Approach 2:
The system performs self-service by automatically generating tissue contours through intensity profile analysis along linear segments radiating from user-selected points. The algorithm independently determines boundary locations based on intensity thresholds and anatomical constraints, reducing reliance on continuous manual input while maintaining segmentation accuracy.
2Adaptability or versatility
If manual segmentation is performed, then adaptability to complex anatomical variations is possible, but ease of operation deteriorates due to high expertise requirements
Solution Approach 1:
The patent applies local quality by allowing users to select seed points at specific anatomical locations where local intensity patterns define tissue boundaries. The system adapts to local anatomical variations by analyzing intensity profiles specific to each seed point location, maintaining versatility while simplifying operation to point selection rather than continuous contouring.
Solution Approach 2:
The system performs preliminary action by pre-defining radial search paths and intensity analysis parameters based on anatomical knowledge. Users only need to select seed points, and the system automatically handles the complex boundary detection along predetermined linear segments, reducing expertise requirements while maintaining adaptability to anatomical variations.
3Productivity
If fully automated segmentation is implemented, then productivity increases, but measurement precision deteriorates due to lack of human expertise input
Solution Approach 1:
The patent applies partial action by requiring minimal user input (seed point selection) rather than full manual contouring, achieving sufficient precision for clinical purposes. The automated intensity profile analysis performs the majority of the segmentation work, balancing productivity gains with maintained measurement accuracy through hybrid operation.
Data Source
AI summary
Systems and methods for segmenting a medical image are provided. In some embodiments, the method includes receiving an image acquired from at least a portion of a subject's anatomy, selecting a first point within a target structure identified on the image, and determining a second and a third point associated with extents of a target biological tissue, the first, second and third points defining a sector described by the first point, a central angle and a subtended arc. The method also includes determining a plurality of arc points along the subtended arc, and extending a plurality of linear segments along directions determined by the first point and the plurality of arc points. The method further includes determining an intensity profile along each linear segment to identify boundaries of the target biological tissue, and generating a contour of the target biological tissue using the identified boundaries.