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Spatial-Intensity Transforms for Medical Image-to-Image Translation.
IEEE Transactions on Medical Imaging
|June 7, 2023
Summary
We developed a spatial-intensity transform (SIT) to enhance medical image translation, improving image quality and robustness for AI models. This method aids in visualizing and forecasting changes in brain MRI scans for neurodegenerative diseases and stroke patients.
Area of Science:
- Computer Vision
- Medical Imaging
- Artificial Intelligence
Background:
- Image-to-image translation in computer vision faces challenges with medical images due to artifacts and limited data.
- Conditional generative adversarial networks (cGANs) performance degrades under these conditions.
Purpose of the Study:
- To develop a novel method, spatial-intensity transform (SIT), to enhance the quality and domain matching of medical image translation.
- To improve the robustness and interpretability of generative models for clinical applications.
Main Methods:
- Introduced SIT, a lightweight, modular network component constraining generators to smooth spatial transforms (diffeomorphisms) with sparse intensity changes.
- Applied SIT to various architectures and training schemes for medical image analysis.
Main Results:
- SIT significantly improved image fidelity and model generalization across different scanners compared to unconstrained methods.
- The technique provided disentangled anatomical and textural changes, aiding interpretation of predictions.
- Demonstrated effectiveness on predicting longitudinal brain MRIs in neurodegeneration and visualizing age/stroke effects in clinical brain scans.
Conclusions:
- SIT offers a simple, powerful technique to enhance robustness in conditional generative models for medical imaging.
- This approach is critical for translating AI visualization and forecasting tools into clinical settings.
- SIT accurately forecasts brain aging and captures associations between ventricle expansion, aging, white matter hyperintensities, and stroke severity.
