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Synthetic CT evaluation
- Paolo Zaffino (Magna Graecia University of Catanzaro, Italy)
- Maria Francesca Spadea (Magna Graecia University of Catanzaro, Italy)
- Everyone else wants to join
Several algorithms for MRI to synthetic CT have been proposed.
Each group quantifies conversion accuracy in different ways, making difficult to compare algorithms.
We want to create a Slicer module for stardazied conversion accuracy assessment.
- Implement in 3D Slicer the validation workflow proposed in Spadea, M.F., Pileggi, G., Zaffino, P., Salome, P., Catana, C., Izquierdo-Garcia, D., Amato, F. and Seco, J., 2019. Deep Convolution Neural Network (DCNN) Multiplane Approach to Synthetic CT Generation From MR images—Application in Brain Proton Therapy. International Journal of Radiation Oncology* Biology* Physics, 105(3), pp.495-503.
Approach and Plan
- Write an extension that will quantify conversion accuracy. The code will be written in python.
Progress and Next Steps
- Describe specific steps you have actually done.
Background and References