3D Image Segmentation for TTFields Array Placement Planning
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Solution Overview
Problem
Existing image segmentation tools for Tumor Treating Fields (TTFields) treatment planning are complex, require multiple screens and interfaces, and struggle to effectively limit segmentation to specific regions or boundaries within images, making them inefficient for optimizing TTFields therapy.
Innovation Solution
A method for segmenting a three-dimensional model to create a patient model using a patient modeling application that analyzes imaging data to optimize transducer array placement, employing finite element method simulations and semi-automatic image segmentation tools to generate a three-dimensional array layout map for TTFields therapy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If traditional image segmentation tools are used for TTFields treatment planning, then segmentation can be performed, but the process requires multiple screens and interfaces, making it complex and inefficient
Solution Approach 1:
The patent combines multiple segmentation functions and tools into a single integrated user interface. The system merges 2D and 3D segmentation capabilities, along with various editing tools and visualization features, into one unified interface that eliminates the need for multiple separate screens and interfaces, thereby improving ease of operation while maintaining comprehensive functionality
Solution Approach 2:
The user interface is designed to perform multiple segmentation and analysis functions within a single system. It provides universal tools that can handle different segmentation tasks (2D and 3D), offer various editing capabilities, and support multiple visualization modes, reducing the need for separate specialized tools and interfaces
2Manufacturing precision
If traditional image segmentation tools are used, then segmentation can be performed, but they are unable to effectively limit segmentation to specific regions or boundaries within images
Solution Approach 1:
The system implements local quality control by allowing users to define specific regions of interest and apply segmentation constraints to particular areas of the image. The interface provides tools to set boundaries, exclude certain regions, and focus segmentation operations on specific anatomical structures, enabling precise local control over where segmentation occurs within the image
Solution Approach 2:
The patent employs segmentation of the user interface itself into functional zones that correspond to different segmentation tasks and regions. This includes separate control panels for 2D and 3D segmentation, boundary definition tools, and region-specific editing capabilities, allowing users to systematically manage complex segmentation tasks through organized interface segments
3Adaptability or versatility
If traditional image segmentation tools are used, then segmentation can be performed, but complex actions and procedures are required to assign structures within images to different structures
Solution Approach 1:
The system provides dynamic structure assignment capabilities where the user interface adapts based on the current segmentation state and user actions. Structures can be dynamically created, modified, merged, or split through intuitive interface commands, allowing the system to respond flexibly to different segmentation scenarios without requiring complex procedural steps
Data Source
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AI summary
Methods, systems, and apparatuses are described for interacting with images, segmenting the images, and determining one or more regions of interest within the images.