Difference between revisions of "Project Week 25/CNN for PseudoCT Generation from T1T2 MR"
From NAMIC Wiki
| Line 26: | Line 26: | ||
Analysis of the different output with MAE and Bias metrics | Analysis of the different output with MAE and Bias metrics | ||
|<!-- Progress and Next steps (fill out at the end of project week), bullet points --> | |<!-- Progress and Next steps (fill out at the end of project week), bullet points --> | ||
| − | + | The most suited CNN to be used resulted an edited U-Net that does not make use of the final Dense Layer, useful for segmentation but not for regression purpose. | |
| + | Also unpooling layer was used in the decoding part, with zero-filling instead of repetitions. | ||
|} | |} | ||
Revision as of 09:18, 30 June 2017
Home < Project Week 25 < CNN for PseudoCT Generation from T1T2 MR
Back to Projects List
Key Investigators
- Giampaolo Pileggi (Magna Graecia University, Italy/German Cancer Research Center (DKFZ), Germany)
- Paolo Zaffino (Magna Graecia University, Italy)
- Salvatore Scaramuzzino (Magna Graecia University/ASL Vercelli, Italy)
- Maria Francesca Spadea (Magna Graecia University, Italy)
- Gino Gulamhussene (University of Magdeburg, Germany)
- Anneke Meyer (University of Magdeburg, Germany)
Project Description
This tool allows the user to generate an HU map (Pseudo-CT) from T1/T2 input MRI. The tool is still in the early development stages with good early results in terms of Mean Absolute Error and Bias.
| Objective | Approach and Plan | Progress and Next Steps |
|---|---|---|
|
The main object is to understand which topology of CNN is the most suited for the Pseudo-CT generation task. |
Test of different structures with training on low-res images in order to speed-up computational time. Analysis of the different output with MAE and Bias metrics |
The most suited CNN to be used resulted an edited U-Net that does not make use of the final Dense Layer, useful for segmentation but not for regression purpose. Also unpooling layer was used in the decoding part, with zero-filling instead of repetitions. |
Illustrations
