Anatomy-Aware Contour Editing for Medical Imaging
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Solution Overview
Problem
Conventional methods for editing contours in medical imaging, such as MRI, are time-consuming, inefficient, and prone to inaccuracies due to the need for manual annotation and verification of anatomically accurate contours.
Innovation Solution
An anatomy-aware contour editing method that utilizes a processor to receive images, identify anatomically recognizable structures, annotate segments, draw contours, and edit contours based on user input and anatomical information, thereby improving the efficiency and accuracy of contour editing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation and verification of contours is performed, then anatomical accuracy is improved, but time consumption increases
Solution Approach 1:
The system performs preliminary automated segmentation and contour generation before manual verification, preparing the contours in advance so that users only need to verify and make minor adjustments rather than creating contours from scratch, thus reducing time consumption while maintaining accuracy
Solution Approach 2:
The system incorporates feedback mechanisms where automated segmentation results are evaluated against anatomical knowledge bases and previous annotations, allowing the system to learn and improve over time, reducing the need for extensive manual verification while maintaining high anatomical accuracy
2Productivity
If automated segmentation methodology is used, then productivity is improved, but anatomical accuracy deteriorates
Solution Approach 1:
The system introduces an intermediary step between automated segmentation and final contour output, where anatomical knowledge bases and expert rule systems mediate the automated results, correcting anatomical inaccuracies while maintaining the efficiency benefits of automation
Solution Approach 2:
The system combines multiple approaches (automated segmentation algorithms, anatomical knowledge bases, expert rules, and interactive verification) into a composite solution that leverages the strengths of each component to achieve both high productivity and anatomical accuracy
3Manufacturing precision
If user continuously interacts with contours for fine adjustments, then contour precision is improved, but ease of operation deteriorates
Solution Approach 1:
The system incorporates self-service features where the automated segmentation and contour generation processes perform fine adjustments automatically based on anatomical patterns and previous user interactions, reducing the need for continuous manual fine-tuning while maintaining high precision
Data Source
AI summary
An anatomy-aware contouring editing method includes receiving an image, wherein the image represents an anatomically recognizable structure; identifying a first image segment representing part of the anatomically recognizable structure; annotating the first image segment to generate a label of the part; drawing a contour along a boundary of the part; receiving a first input from a user device indicative of a region of contour failure, wherein the region of contour failure includes a portion of a contour that requires editing; editing the contour for generating an edited contour based on the first input and anatomical information; and updating another contour of another part of the anatomically recognizable structure based on the edited contour, wherein the another part is anatomically related to the part.