Automated ROI Selection in Medical Images Using Morphometrics
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Solution Overview
Problem
Existing manual and semi-manual methods for selecting regions of interest (ROIs) in medical images are labor-intensive, variable, and prone to inaccuracies, leading to invalid assessments and compromised treatment efficacy due to inconsistent ROI selection across different operators and studies.
Innovation Solution
A computer-implemented method and system using deep learning and machine learning algorithms for precise and reproducible ROI selection, involving image pre-processing, segmentation, morphometric measurement, and normalization to determine the shape and size of ROIs based on detected landmarks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual or semi-manual methods are used for ROI selection, then operator flexibility and initial input capability are maintained, but labor intensity increases and ROI selection variability worsens
Solution Approach 1:
The system performs ROI selection automatically using trained machine learning models without requiring operator intervention. The algorithm independently identifies anatomical landmarks, performs segmentation, and determines ROI boundaries based on morphometric measurements, enabling the system to serve itself rather than relying on manual operator input.
Solution Approach 2:
The patent replaces manual mechanical operations (operator drawing of contours and landmark identification) with automated computational algorithms. Machine learning models process images to automatically detect landmarks and define ROIs, substituting human manual work with automated image processing and morphometric analysis systems.
2Adaptability or versatility
If manual or semi-manual methods are used for ROI selection, then operator judgment can be applied, but ROI selection variability and measurement precision worsen
Solution Approach 1:
The system uses morphometric parameters (lengths, areas, shapes) derived from image analysis to objectively define ROI boundaries. By changing from subjective operator judgment to objective parameter-based selection, the system achieves consistent and reproducible ROI definitions across different operators and studies.
Solution Approach 2:
The system incorporates validation mechanisms where morphometric measurements are used to verify ROI correctness. The automated feedback loop ensures that selected ROIs meet predefined criteria based on anatomical measurements, improving measurement precision and consistency.
3Ease of manufacture
If heuristic rules-based selection is used, then simple implementation is achieved, but clinical relevance and manufacturing precision worsen
Solution Approach 1:
The system performs preliminary morphometric measurements and landmark detection before finalizing ROI selection. By conducting these preparatory analytical steps first, the system ensures that ROI boundaries are defined based on accurate anatomical measurements, improving selection precision while maintaining automated simplicity.
Solution Approach 2:
The patent segments the image into distinct anatomical regions based on detected landmarks and morphometric analysis. This segmentation process divides the complex task of ROI selection into manageable steps: landmark detection, morphometric measurement, and boundary definition, achieving both simplicity and accuracy.
4Ease of operation
If fixed-size boxes or fixed-distance offsets are used for ROI selection, then ease of operation is maintained, but adaptability to individual patient anatomy and measurement precision worsen
Solution Approach 1:
The system dynamically adjusts ROI size and position based on individual patient anatomy rather than using fixed parameters. Morphometric measurements from each patient's specific bone structure determine the optimal ROI dimensions, making the selection process adaptive to anatomical variations while remaining automated.
Solution Approach 2:
The patent applies local morphometric measurements to each specific anatomical region being analyzed. Instead of using universal fixed-size boxes, the system determines ROI characteristics based on local anatomical features and measurements, ensuring each ROI is appropriately sized and positioned for that specific patient's anatomy.
Data Source
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AI summary
A computer-implemented method and system for selecting one or more regions of interest (ROIs) in an image. The method comprises: identifying one or more objects of interest that have been segmented from the image; identifying predefined landmarks of the objects; determining reference morphometrics pertaining to the objects by performing morphometrics on the objects by reference to the landmarks; selecting one or more ROIs from the objects according to the reference morphometrics, comprises identifying the location of the ROIs relative to the reference morphometrics; and outputting the selected one or more ROIs.