Automated Cardiac CT Segmentation via Deep Learning
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Solution Overview
Problem
Current methods for cardiac computed tomography (CT) imaging require manual annotation for accurate segmentation and plane re-slicing, leading to inter-reader variability and inefficiency, which limits clinical utility and reproducibility in assessing cardiac function and structure.
Innovation Solution
The implementation of a deep learning-based method using a convolutional neural network (CNN) for automated segmentation and re-slicing of cardiac CT images, enabling the identification of heart chambers and standard imaging planes without manual interaction, leveraging a modified U-Net architecture for accurate volume prediction and plane orientation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation is used for cardiac CT image segmentation and plane re-slicing, then accuracy can be achieved, but inter-reader variability and inefficiency occur
Solution Approach 1:
The patent replaces the manual mechanical annotation process with an automated deep learning system. A trained neural network model automatically performs cardiac chamber segmentation and plane re-slicing on CT images, eliminating human reader variability while maintaining high accuracy. The system processes images through convolutional neural networks that have been trained on manually annotated datasets, thereby substituting human expertise with an automated algorithmic approach.
2Measurement precision
If manual annotation is used for cardiac CT image segmentation, then accurate segmentation can be obtained, but time-consuming and inefficient processes result
Solution Approach 1:
The manual annotation process is replaced by an automated deep learning system that processes cardiac CT images rapidly. The neural network model, trained on manually annotated data, automatically performs segmentation and plane re-slicing without requiring human intervention for each image, thereby dramatically improving throughput and efficiency while maintaining accuracy.
Solution Approach 2:
The system performs preliminary training on manually annotated datasets to create a trained model. Once trained, this model can automatically process new images without requiring manual annotation for each case. The preliminary manual work is done once during training, enabling rapid automated processing of subsequent images.
3Productivity
If automated deep learning methods are used for cardiac segmentation, then productivity and reproducibility improve, but implementation complexity increases
Solution Approach 1:
The patent implements an automated deep learning system that replaces manual processes, accepting cardiac CT images as input and producing segmented images with plane re-slicing as output. The system includes a trained neural network model that performs both segmentation and plane identification tasks automatically, improving productivity and reproducibility despite the increased computational complexity.
Data Source
AI summary
Devices, systems, and methods for automated segmentation and slicing of cardiac computed tomography (CT) images are described. An example method includes receiving a first plurality of input image frames associated with a cardiac CT operation, each of the first plurality of input image frames comprising a representation of two or more chambers of a heart, and performing, using a convolutional neural network, a segmentation operation and a slicing operation on each of the first plurality of input image frames to generate each of a plurality of output image frames comprising results of the segmentation operation and the slicing operation, wherein the segmentation operation comprises identifying volumes of each of the two or more chambers of the heart based on blood volumes, wherein the slicing operation comprises identifying one or more features of the heart in at least one predefined plane in a coordinate system associated with the cardiac CT operation.