Cardiac MRI Segmentation with Anatomical Plausibility Analysis
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Solution Overview
Problem
Current manual approaches for cardiac MRI segmentation are time-consuming, prone to subjective errors, and produce anatomically implausible results, limiting their clinical usability.
Innovation Solution
A computerized method and system utilizing a whole volume segmentation analysis module, 3D volume assembly module, and anatomic plausibility analysis module, integrated with a UNet convolutional network architecture and adversarial variational autoencoder, to automate cardiac MRI segmentation and ensure anatomical plausibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual approaches are used for cardiac MRI segmentation, then anatomical expertise can be applied, but the process is time-consuming and tedious
Solution Approach 1:
The patent replaces the manual mechanical segmentation process with an automated deep learning system. The UNet-based convolutional neural network automatically segments cardiac MRI images, eliminating the need for manual tracing while maintaining anatomical plausibility through learned patterns from training data.
Solution Approach 2:
The system enables self-service automation where the segmentation process performs itself without human intervention. The trained model automatically processes new cardiac MRI images, generating segmentations independently, thus freeing clinicians from tedious manual work while maintaining consistent quality.
2Productivity
If automated convolutional neural networks are used for segmentation, then time and labor costs are reduced, but the results may be anatomically improbable and medically implausible
Solution Approach 1:
The patent implements feedback mechanisms through loss functions that incorporate anatomical constraints. The model receives feedback during training about anatomical plausibility, adjusting its predictions to satisfy physiological constraints such as realistic ventricular shapes and proper spatial relationships between cardiac structures.
Solution Approach 2:
The system changes the parameter space by incorporating anatomical constraint parameters into the learning objective. By modifying the loss function to include terms that enforce anatomical plausibility (such as shape regularity, spatial relationships, and physiological consistency), the model learns to generate medically valid segmentations while maintaining automation benefits.
3Measurement precision
If manual segmentation is performed by trained experts, then anatomical accuracy can be achieved, but subjectivity and inter-observer variability introduce errors
Solution Approach 1:
The patent creates a digital copy of expert knowledge embedded in the trained neural network model. Once trained on annotated data, the model consistently reproduces expert-level segmentation quality across different users and time points, eliminating inter-observer variability while maintaining high measurement precision through the learned segmentation patterns.
Data Source
AI summary
A method and system for image segmentation systems and related methods of automatically segmenting cardiac MRI images using deep learning methods. One example method includes inputting MRI volume data from a MRI scanner, segmenting the MRI volume data with a whole volume segmentation analysis module, assembling the segmented MRI volume data into a 3D volume assembly with a 3D volume assembly module, determining the 3D volume assembly for anatomic plausibility with an anatomic plausibility analysis module, and outputting a final segmented 3D volume assembly.