Deep Learning Myocardium Segmentation for 17-Segment Cardiac Targets
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Solution Overview
Problem
Defining the treatment target for stereotactic body radiation therapy (SBRT) or stereotactic ablative radiotherapy (SABR) in cardiac arrhythmias, such as ventricular tachycardia, is challenging due to difficulties in integrating electrophysiological information with radiation treatment planning images, leading to time-consuming and error-prone manual segmentation of the 17-segment contours with high inter-observer variability.
Innovation Solution
A system and method for automatic segmentation of the cardiac myocardium using a deep learning neural network to generate a patient-specific 17-segment myocardial contour model based on heart images and anatomical landmarks, facilitating accurate target delineation for radiation therapy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual segmentation of 17-segment contours is performed, then anatomical accuracy can be maintained, but the process becomes time-consuming and error-prone with high inter-observer variability
Solution Approach 1:
The patent replaces the manual mechanical segmentation process with an automated deep learning-based system. The neural network automatically processes cardiac images and generates 17-segment contours without human intervention, eliminating the time-consuming manual tracing process while maintaining anatomical accuracy through learned patterns from training data.
Solution Approach 2:
The system creates a digital copy of the manual segmentation process through the trained neural network model. The model learns from annotated training images and reproduces the segmentation task automatically, preserving the anatomical precision of manual methods while eliminating the time investment and variability associated with repeated manual operations.
2Measurement precision
If manual segmentation of 17-segment contours is performed, then anatomical accuracy can be maintained, but inter-observer variability increases
Solution Approach 1:
The system transforms the segmentation process from a human-dependent parameter (observer experience, fatigue, skill level) to a computational parameter (neural network output). By changing the fundamental parameter of who performs the segmentation from human to algorithm, the system eliminates inter-observer variability while maintaining consistent anatomical accuracy through reproducible computational results.
Solution Approach 2:
The patent replaces the mechanical manual segmentation process with an automated deep learning-based system. The neural network automatically processes cardiac images and generates 17-segment contours without human intervention, eliminating the time-consuming manual tracing process while maintaining anatomical accuracy through learned patterns from training data.
3Productivity
If automated segmentation using deep learning is implemented, then segmentation time is reduced to less than 5 minutes, but the complexity of the system increases
Solution Approach 1:
The system performs preliminary training of the neural network on annotated cardiac images before actual use. This preliminary action of training and model development is completed once, after which the automated segmentation can be rapidly executed. The heavy computational complexity is concentrated in the training phase, while the actual segmentation process becomes fast and simple to execute.
Solution Approach 2:
The patent replaces the manual mechanical segmentation process with an automated deep learning-based system. The neural network automatically processes cardiac images and generates 17-segment contours without human intervention, eliminating the time-consuming manual tracing process while maintaining anatomical accuracy through learned patterns from training data.
4Loss of time
If automated segmentation is used, then segmentation time is reduced and reliability is improved, but the difficulty of integrating electrophysiological information with radiation treatment planning images remains
Solution Approach 1:
The system segments the cardiac anatomy into the standard 17 segments, providing a structured framework that can be systematically integrated with electrophysiological data. By dividing the heart into discrete, standardized regions, the patent creates a common language and reference structure that facilitates the integration of multiple data types including electrophysiological information and radiation treatment planning.
Solution Approach 2:
The automated segmentation system acts as an intermediary that processes and standardizes anatomical information, creating a unified representation that can bridge electrophysiological data and radiation treatment planning. The 17-segment model serves as a common reference framework that mediates between different data modalities, enabling their integration despite their different natures and acquisition methods.
Data Source
AI summary
A method for segmentation of a cardiac myocardium in one or more images of a subject includes receiving at least one image of a heart of the subject, a segmentation of at least one heart structure, and an identification of a right ventricle insertion point, providing the at least one image of a heart of the subject, the segmentation of the at least one heart structure, and the identification of a right ventricle insertion point to a segmentation model, and generating, using the segmentation model, a subject specific seventeen segment myocardial contour model.