Automated Control Point Adjustment for Medical Image Annotation
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Solution Overview
Problem
Current data annotation methods require extensive manual adjustment of control points to achieve satisfactory contour annotations, which is time-consuming, prone to errors, and inefficient.
Innovation Solution
An apparatus and method for automated control point adjustment in data annotation, where a processor automatically adjusts the positioning of control points identifying contours of a medical image for a segmentation mask based on user input and image partitions, reducing human labelling effort.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual adjustment of control points is used to achieve satisfactory contour annotations, then annotation accuracy can be improved, but time consumption and human effort increase significantly
Solution Approach 1:
The system enables self-service by automatically adjusting control points based on user input and image partitions. When a user adjusts one control point, the system autonomously recalculates and repositions adjacent control points to maintain contour accuracy, eliminating the need for manual adjustment of every control point while preserving annotation precision
Solution Approach 2:
The system implements feedback by continuously monitoring control point adjustments and automatically propagating changes to adjacent control points. This closed-loop approach ensures that contour annotations remain accurate by dynamically adjusting control points based on the current state of the annotation, reducing both time consumption and manual effort
2Measurement precision
If extensive manual adjustment of control points is performed, then contour accuracy can be improved, but annotation efficiency deteriorates
Solution Approach 1:
The system performs self-service by automatically maintaining contour accuracy through programmatic adjustment of control points. The automatic adjustment mechanism ensures that contour accuracy is preserved while eliminating the need for extensive manual intervention, thereby significantly improving annotation efficiency
Solution Approach 2:
The system applies preliminary action by pre-calculating and positioning control points based on image partitions before user interaction. This preliminary setup reduces the number of adjustments needed during the annotation process, improving efficiency while maintaining accuracy
3Measurement precision
If manual control point adjustment is used, then annotation detail can be improved, but human error increases
Solution Approach 1:
The system eliminates human error by performing automatic control point adjustment without manual intervention. The programmatic approach ensures consistent and reliable annotation details by removing the variable of human mistake while maintaining the precision required for detailed annotations
Solution Approach 2:
The system replaces the mechanical manual adjustment process with an automated computational system. This substitution eliminates human error by using algorithmic calculations to determine control point positions, ensuring reliable and consistent annotation details
Data Source
AI summary
An automated process for data annotation of medical images includes obtaining image data from an imaging sensor, partitioning the image data, identifying an object of interest in the partitioned image data, generating an initial contour with one or more control points with respect to the object of interest, identifying a manual adjustment of one of the control points, automatically adjust a position of at least one other control point within a predetermined range of the manually adjusted control point to a new position, the new position of the at least one other control point and manually adjusted control point defining a new contour, and generating an updated image with the new contour and corresponding control points.