Automated ROI Generation for TTFields Treatment Planning
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Solution Overview
Problem
Current tumor treating field (TTFields) treatment planning is inefficient due to the manual identification of a region of interest (ROI) in medical images, which is time-consuming and costly, causing delays in treatment planning, especially when multiple images are involved.
Innovation Solution
An automated method for generating a ROI in medical images using a computer-based system, where a user manually segments a minimum number of voxels, and the system automatically determines and expands the ROI, reducing the need for manual processing and enabling faster treatment planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual identification of ROI is used in medical images, then accuracy of treatment planning is maintained, but time consumption and cost increase significantly
Solution Approach 1:
The patent introduces an automated image processing system that acts as an intermediary between the medical image data and the treatment planning decision. This system uses algorithms to automatically identify and segment the ROI, eliminating the need for manual processing while maintaining accuracy through validated computational methods.
Solution Approach 2:
The patent replaces the manual mechanical process of ROI identification with an automated computational system. The manual segmentation task is substituted by computer-based image processing algorithms that can rapidly analyze medical images and generate treatment plans without human intervention.
2Reliability
If manual ROI segmentation is performed for multiple medical images, then treatment accuracy is ensured, but productivity decreases due to repetitive manual work
Solution Approach 1:
The patent enables the system to perform ROI identification autonomously without requiring manual intervention for each image. The automated system processes multiple medical images independently and consistently, maintaining reliability through standardized algorithms while dramatically improving productivity by eliminating repetitive manual work.
Solution Approach 2:
The patent implements preliminary automated processing of medical images to pre-identify ROIs before final treatment planning. This preliminary action prepares the data in advance, allowing clinicians to review and approve pre-segmented regions rather than performing manual segmentation from scratch, thereby maintaining accuracy while accelerating the overall process.
3Productivity
If automated ROI generation is implemented, then processing speed increases, but system complexity increases
Solution Approach 1:
The patent implements a universal automated processing system that can handle multiple types of medical images and various ROI identification tasks through a single integrated platform. This multi-functional approach increases productivity across different scenarios while managing complexity through standardized, reusable algorithms rather than requiring separate systems for each task.
Data Source
AI summary
A computer-implemented method for treatment planning for administering tumor treating fields to a subject, the method comprising: presenting on a display a slice through a medical image of the subject, wherein the medical image comprises voxels; and determining a segmentation of a minimum number of voxels of at least one tissue type in the slice of the medical image; receiving a user selection to automatically generate a region of interest (ROI) in the medical image for application of tumor treating fields to the subject; and automatically generating the region of interest in the medical image for application of tumor treating fields to the subject based on the segmentation of the minimum number of voxels of at least one tissue type in the slice of the medical image.