# Difference between revisions of "Documentation/Nightly/Modules/DiceComputation"

Home < Documentation < Nightly < Modules < DiceComputation

# Introduction and Acknowledgements

This work is supported by NA-MIC, NCIGT, and the Slicer Community.
Author: Laurent Chauvin, Nobuhiko Hata
Contact: Laurent Chauvin <email> lchauvin@bwh.harvard.edu</email>

 NA-MIC
 NCIGT
 SPL

This project is supported by National Institute of Health (5P01CA067165, 5R01CA124377, 5R01CA138586, 2R44DE019322, 7R01CA124377, 5R42CA137886, 8P41EB015898).

# Module Description

Dice's coefficient is used to measure the similarity of two datasets together. If Dice's coefficient is 1, datasets are identical, is coefficient is 0, datasets are not overlapping at all. DiceComputation is a Slicer4 module designed to compute several Dice's coefficients and display them to immediately visualize results. Number of label maps used could be adjusted dynamically.

# Use Cases

DiceComputation could be useful to quantitatively compare several label maps. It could be used to compare several algorithms with ground truth, or automated segmentation vs manual.

N/A

# Panels and their use

 DiceComputation GUI
1. Parameters
• Number of label maps to use for computation
2. Label Maps
• Select labels maps (only label maps could be selected) to use for computation or None
• Compute Dice coefficient: Press button to start computation
3. Results
• Once computation is over, results will appear in an array
• Red stripes indicate no label map have been selected (None)
• Green stripes indicate this is Dice's coefficient of a label map with itself (always 1.0, so not displayed)
• Values represent Dice's similarity coefficient. The bigger the value is, the greener the background is, in order to quickly find higher matches.

N/A