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Segmentation of bones in medical dual-energy CT volumes using the 3D U-Net convolutional neural network - supplementary data
Linköping University, Department of Medical and Health Sciences, Division of Radiological Sciences. Linköping University, Faculty of Medicine and Health Sciences.
2018 (English)Data set
Physical description [en]

The data archives contain animated GIFs showing the results of bone segmentation. The algorithm_comparison.rar archive shows the ground truth and results obtained using the JJ2016 and 3D-Unet algorithms. The DECT_comparison.rar archive shows the difference between results obtained from mixed and DECT volumes for the 3D-Unet algorithm. More information is in the report Segmentation of bones in medical dual-energy CT volumes using the 3D U-Net convolutional neural network by José Carlos González Sánchez.

Place, publisher, year
Linköping: Linköping University Electronic Press, 2018.
Version
1.0
National Category
Radiology, Nuclear Medicine and Medical Imaging Computer Sciences
Identifiers
URN: urn:nbn:se:liu:diva-150979OAI: oai:DiVA.org:liu-150979DiVA, id: diva2:1246352
Available from: 2018-09-07 Created: 2018-09-07 Last updated: 2018-09-21Bibliographically approved

Open Access in DiVA

Algorithm comparison and DECT comparison(1712469 kB)35 downloads
File information
File name DATASET01.zipFile size 1712469 kBChecksum
Type datasetMimetype application/zip

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González Sánchez, José Carlos
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Division of Radiological SciencesFaculty of Medicine and Health Sciences
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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
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Output format
  • html
  • text
  • asciidoc
  • rtf