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SlicerModalityConverter Extension - addition of new models and use case examples

Key Investigators

• Ciro Benito Raggio (Karlsruhe Institute of Technology, Germany)
• Paolo Zaffino (Magna Graecia University of Catanzaro, Italy)
• Maria Francesca Spadea (Karlsruhe Institute of Technology, Germany)

Project Description

SlicerModalityConverter is a 3D Slicer extension designed for medical image-to-image (I2I) translation.

The ModalityConverter module provides a user-friendly interface for integrating multiple AI models trained for I2I translation (currently MRI-to-CT). It also supports GPU acceleration for faster inference, and is designed to allow users to easily integrate custom models.

More about the module here: https://github.com/ciroraggio/SlicerModalityConverter

Objective

1. Integration of new translation models for T1w-to-T2w MRI translation.
2. Creating use case examples with video tutorials.

Approach and Plan

1. Integrate two new pre-trained models for T1w-to-T2w translation (presented in this study and released in this repository), following the guidelines reported in the module documentation for integrating custom models.

2. Create video tutorials demonstrating the common uses of existing models. For example, show how to use MRI-to-synthetic CT translation models to extract the skull’s representation from a T1w brain MRI.

Progress and Next Steps

1. Describe specific steps you have actually done.

Illustrations

No response

Background and References

No response