Medical Image ROI Segmentation via Control Point Outlines

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Solution Overview

Problem

Traditional methods of medical image segmentation require manual editing, which is time-consuming and costly, and can lead to inconsistent results when different methods or annotators are used.

Innovation Solution

A system that automatically segments a region of interest (ROI) in a medical image by obtaining an outline based on control points and allowing user input to adjust the outline, ensuring consistency and efficiency.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual editing is used for medical image segmentation, then segmentation accuracy can be achieved, but time consumption and cost increase significantly

Engineering Contradiction:

Improvesegmentation accuracyVSAvoidmanual editing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system enables automatic self-segmentation of medical images by having the computer execute segmentation algorithms autonomously based on image data, eliminating the need for manual editing while maintaining consistent segmentation results across different images and users

Inventive Principle:

Principle #25Self-service

2Adaptability or versatility

If different manual segmentation methods or annotators are used, then various segmentation results can be obtained, but consistency between results deteriorates

Engineering Contradiction:

Improvesegmentation method flexibilityVSAvoidsegmentation result consistency

Core Design Contradiction:

Adaptability or versatilityVSStability of the object's composition

Solution Approach 1:

The system changes the fundamental parameter from manual annotation to automated algorithmic processing, where the computer executes consistent segmentation algorithms on all images. This ensures that segmentation results are determined by objective computational parameters rather than subjective human judgment, thereby maintaining high consistency across different images while preserving adaptability through configurable segmentation parameters

Inventive Principle:

Principle #35Parameter changes

3Manufacturing precision

If traditional manual segmentation methods are used, then detailed control over segmentation can be achieved, but productivity decreases

Engineering Contradiction:

Improvesegmentation detail controlVSAvoidsegmentation processing speed

Core Design Contradiction:

Manufacturing precisionVSProductivity

Solution Approach 1:

The system replaces the mechanical manual editing process with automated computer-based image processing algorithms. The computer analyzes image data and automatically generates segmentation results, substituting human manual operations with computational processes that are both faster and more consistent, thereby improving productivity while maintaining segmentation quality through algorithmic precision

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Data Source

PatentUS20250111517A1Segmentation of a region of interest in a medical image

Publication Date: 2025.04.03 SHANGHAI UNITED IMAGING INTELLIGENCE CO LTD

AI summary

An apparatus for annotating a medical image may be configured to obtain, automatically, an outline of a region of interest (ROI) in the medical image and determine, based on one or more inner control points and one or more outer control points. The one or more inner control points may be located within the ROI and the one or more outer control points may be located outside of the ROI. The outline may be subsequently adjusted based on a user input and the adjusted outline may be used to generate a segmentation of the ROI.