Anatomical Shape Modeling With Hybrid Neural Reconstruction
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Solution Overview
Problem
Current medical imaging technologies, such as MRI and CT, produce lower resolution 3D reconstructions and require manual interpretation by radiologists, while existing shape models like SSMs and implicit NSMs lack accuracy in disease classification and quantification of localized biomarkers.
Innovation Solution
A hybrid explicit-implicit neural shape model (NSM) combining a convolutional neural network and a multilayer perceptron to generate explicit and implicit representations of anatomical structures, enabling higher resolution 3D models and accurate disease classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current medical imaging technologies (MRI and CT) are used to generate 3D reconstructions, then anatomical structures can be visualized, but the resolution is lower and manual interpretation is required
Solution Approach 1:
The patent replaces manual radiologist interpretation with an automated neural shape model system. The hybrid explicit-implicit NSM automatically processes imaging data to generate high-resolution 3D reconstructions and performs disease classification, substituting the mechanical/manual process of radiological interpretation with an automated computational system that delivers both higher resolution and automated analysis.
2Reliability
If existing shape models (SSMs and implicit NSMs) are used for disease classification, then anatomical modeling is achieved, but accuracy in disease classification and quantification of localized biomarkers is insufficient
Solution Approach 1:
The patent merges explicit and implicit neural shape modeling approaches into a hybrid system. This combination integrates the strengths of both methods: explicit models provide accurate geometric representation while implicit models enable efficient querying and classification. The merged architecture achieves superior disease classification accuracy and precise quantification of localized biomarkers compared to either approach alone.
Data Source
AI summary
Systems and methods for anatomical shape modeling in accordance with embodiments of the invention are illustrated. One embodiment includes a method for clinical anatomy modeling, comprising obtaining imaging data of a portion of a patient's anatomy, generating a first model of the patient's anatomy based on the imaging data, providing the first model to a trained hybrid explicit-implicit neural shape model (NSM), obtaining a latent representation of the first model from the trained hybrid explicit-implicit NSM, and reconstructing a second model of the portion of the patient's anatomy using the latent representation. In a further embodiment, the second model is at a higher resolution than the first model. In a yet further embodiment, the method further includes steps for providing the latent representation to a classification model trained to classify latent representations to clinical values.