Fully Convolutional Network for Cardiac MRI Segmentation
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Solution Overview
Problem
Current methods for cardiac MRI segmentation, such as the 'snakes' algorithm, are inefficient and prone to errors due to reliance on manual input, limited representation capacity, and inability to accurately distinguish papillary muscles from the blood pool, leading to inconsistent measurements and increased diagnostic time.
Innovation Solution
A machine learning system utilizing a fully convolutional neural network (CNN) for automated segmentation of cardiac structures, which includes a contracting path and an expanding path with convolutional and upsampling layers, and uses hyperparameter configurations to optimize segmentation accuracy, particularly in distinguishing papillary muscles and handling missing landmarks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual contouring methods are used for ventricular segmentation, then measurement accuracy can be maintained through expert judgment, but the process requires 10 minutes or more per case and involves significant manual effort
Solution Approach 1:
The patent replaces the mechanical manual contouring process with a deep learning-based automated segmentation system. The neural network model processes cardiac MRI images to automatically generate ventricular contours, eliminating the need for manual pixel-by-pixel contour drawing while maintaining measurement accuracy through learned patterns from training data.
Solution Approach 2:
The segmentation system performs self-service by automatically determining end-systole and end-diastole time points without requiring radiologist intervention. The model independently identifies these critical phases by analyzing temporal variations in ventricular volume across the cardiac cycle, thereby eliminating the time-consuming manual cycling through time points.
2Productivity
If semi-automated contour placement tools are used, then some automation is achieved, but manual adjustment is still required particularly for images with noise or artifacts
Solution Approach 1:
The patent replaces semi-automated tools that require manual adjustment with a fully automated deep learning system. The neural network directly generates contours from input images without requiring intermediate manual intervention, even for challenging cases with noise or artifacts, thereby eliminating the ease of operation issues associated with manual adjustment.
Solution Approach 2:
The model employs parameter changes by adjusting its internal weights and biases during training to adapt to various imaging conditions including noise and artifacts. This allows the system to maintain high productivity and accuracy across diverse image quality scenarios without requiring manual parameter adjustment by the operator.
3Extent of automation
If the snakes algorithm is used for automated segmentation, then some automation is achieved, but the algorithm has limited representation capacity and cannot accurately distinguish papillary muscles from the blood pool
Solution Approach 1:
The patent replaces the snakes algorithm with a deep learning-based active contour model. The new system uses learned features from training data to automatically distinguish papillary muscles from the blood pool, achieving both high automation and accurate differentiation without the representation limitations of traditional algorithms.
Solution Approach 2:
The segmentation system uses a composite approach by combining multiple neural network components: a contracting path for feature extraction, an expanding path for contour generation, and skip connections for preserving spatial details. This composite architecture enables accurate papillary muscle differentiation while maintaining full automation.
4Ease of manufacture
If traditional segmentation methods are used, then simplicity of implementation is maintained, but the process lacks adaptability to handle diverse anatomical variations and pathologies
Solution Approach 1:
The patent applies preliminary action by pre-training the neural network model on a large dataset of cardiac MRI images with diverse anatomical variations and pathologies. This preliminary training equips the model with adaptability to handle various anatomical configurations, and the pre-trained model can then be deployed with simple implementation requiring no additional customization for each new case.
Data Source
AI summary
Systems and methods for automated segmentation of anatomical structures (e.g., heart). Convolutional neural networks (CNNs) may be employed to autonomously segment parts of an anatomical structure represented by image data, such as 3D MRI data. The CNN utilizes two paths, a contracting path and an expanding path. In at least some implementations, the expanding path includes fewer convolution operations than the contracting path. Systems and methods also autonomously calculate an image intensity threshold that differentiates blood from papillary and trabeculae muscles in the interior of an endocardium contour, and autonomously apply the image intensity threshold to define a contour or mask that describes the boundary of the papillary and trabeculae muscles. Systems and methods also calculate contours or masks delineating the endocardium and epicardium using the trained CNN model, and anatomically localize pathologies or functional characteristics of the myocardial muscle using the calculated contours or masks.