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A Generative Shape Compositional Framework to Synthesize Populations of Virtual Chimeras.
IEEE Transactions on Neural Networks and Learning Systems
|March 19, 2024
Summary
This study introduces a novel generative model to create realistic virtual organ populations from incomplete imaging data. This method enhances the plausibility and variability of virtual organs for medical device testing.
Area of Science:
- Medical imaging and computational anatomy
- Machine learning for healthcare applications
- Biomedical engineering and device development
Background:
- Generating diverse and plausible virtual organ populations is crucial for in silico trials (ISTs) of medical devices.
- Existing methods struggle with anatomical data scarcity and variability due to differing imaging modalities and clinical pathways.
- Missing or partially overlapping anatomical information is common across individuals in population datasets.
Purpose of the Study:
- To develop a generative shape model capable of synthesizing complete multipart anatomical structures from unpaired and incomplete datasets.
- To create "virtual chimeras" (VCs) representing plausible and variable organ assemblies.
- To address the challenge of data heterogeneity in medical imaging for population studies.
Main Methods:
- A graph neural network-based generative shape compositional framework was proposed, comprising a part-aware generative model and a spatial composition network.
- A novel self-supervised learning scheme was developed to train the spatial composition network using partially overlapping data and weak labels.
- The framework was applied to cardiac structures derived from UK Biobank cardiac magnetic resonance (MR) imaging data.
Main Results:
- The proposed generative model successfully synthesized complete multipart shape assemblies (virtual chimeras).
- The approach demonstrated superior generalizability and specificity compared to a principal component analysis (PCA)-based shape model when trained with complete and partially overlapping data.
- Synthesized cardiac virtual populations exhibited greater plausibility and captured more shape variability.
Conclusions:
- The developed generative shape model effectively addresses data incompleteness and heterogeneity in anatomical datasets.
- The "virtual chimera" approach offers a superior method for generating realistic organ populations for in silico trials.
- This work advances the capability to create comprehensive virtual populations for medical device research and development.
