Difference between revisions of "Documentation/Nightly/Extensions/ImageCompare"

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Tag: 2017 source edit
Tag: 2017 source edit
 
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Extension: [[Documentation/{{documentation/version}}/Extensions/ImageCompare|ImageCompare]]<br>
 
Extension: [[Documentation/{{documentation/version}}/Extensions/ImageCompare|ImageCompare]]<br>
Author: Paolo Zaffino, Magna Graecia Univeristy of Catanzaro - Italy ({{collaborator|name|Magna Graecia Univeristy of Catanzaro - Italy}})<br>
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Author: Paolo Zaffino, Magna Graecia Univeristy of Catanzaro - Italy<br>
Contributor1: Maria Francesca Spadea, Magna Graecia Univeristy of Catanzaro - Italy ({{collaborator|name|Magna Graecia Univeristy of Catanzaro - Italy}})<br>
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Contributor1: Maria Francesca Spadea, Magna Graecia Univeristy of Catanzaro - Italy<br>
 
Contact: Paolo Zaffino, <email>p.zaffino@unicz.it</email><br>
 
Contact: Paolo Zaffino, <email>p.zaffino@unicz.it</email><br>
  

Latest revision as of 10:59, 24 January 2020

Home < Documentation < Nightly < Extensions < ImageCompare


For the latest Slicer documentation, visit the read-the-docs.


Introduction and Acknowledgements

Extension: ImageCompare
Author: Paolo Zaffino, Magna Graecia Univeristy of Catanzaro - Italy
Contributor1: Maria Francesca Spadea, Magna Graecia Univeristy of Catanzaro - Italy
Contact: Paolo Zaffino, <email>p.zaffino@unicz.it</email>

This extension is for comparing images. For the moment it contains just a single module for syntethic CT evaluation.

Module Description

  • SyntheticCTEvaluation: This module allows to quantify the similarity between a syntethic CT and a ground truth.


Use Cases

  • Synthetic CT Evaluation

User wants to quantify conversion accuracy of his algorithm for synthetic CT generation

Synthetic CT Evaluation module

Tutorials

  • Synthetic CT Evaluation
1. Load ground truth CT
2. Load synthetic CT
3. Load/generate a mask of patient's outilne
4. Click Apply button

Panels and their use

Synthetic CT Evaluation module UI

Similar Modules

References

  • Spadea MF, Pileggi G, Zaffino P, Salome P, Catana C, Izquierdo-Garcia D, Amato F, Seco J. 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. 2019 Nov 1;105(3):495-503.


Information for Developers

https://github.com/pzaffino/SlicerImageCompare