77 CT abdomen (computed tomography) examinations taken with contrast in venous phase. All cases showed liver malignancies. Manual oncological annotations were made by a radiologist and these were controlled by a second experienced radiologist. All changes with a diameter greater than 5mm were segmented and assumed metastases (cysts excluded as defined by HU). 317 lesions were annotated.
Keywords: Radiology, Liver, Cancer, Computed tomography, Annotated.
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|Cite as||Mischa Woisetschläger, Johan Blomma, Nils Dahlström, Caroline Bivik Stadler, and Daniel Forsberg (2019) Liver data from the Visual Sweden project DROID doi:10.23698/aida/drli|
|Title||Liver data from the Visual Sweden project DROID|
Caroline Bivik Stadler
Siemens CT Scanners
|Phase||Venuous (with contrast)|
AIDA BY license
|Copyright||Copyright 2019 Linköping University, Anders Persson|
|Download||Please contact Mischa Woisetschläger, Joel Hedlund, or Anders Persson to request access.|
Manual oncological annotations was made by a radiologist and these were controlled by a second radiologist. All changes with a diameter greater than 5mm was segmented and as assumed metastases (cysts excluded defined by HU). 317 lesions were annotated.
Free for use in legal and ethical medical diagnostics research. Please contact the copyright holder for terms of access.
AIDA BY license
Copyright 2019 Linköping University, Anders Persson
Permission to use, copy, modify, and/or distribute this data within Analytic Imaging Diagnostics Arena (AIDA) for the purpose of medical imaging research with or without fee is hereby granted, provided that the above copyright notice and this permission notice appear in all copies, and that publications resulting from the use of this data cite the following works:
Mischa Woisetschläger, Johan Blomma, Nils Dahlström, Caroline Bivik Stadler, and Daniel Forsberg (2019) Liver data from the Visual Sweden project DROID doi:10.23698/aida/drli.
Stadler, C.B., Lindvall, M., Lundström, C. et al. Proactive Construction of an Annotated Imaging Database for Artificial Intelligence Training. J Digit Imaging (2020). https://doi.org/10.1007/s10278-020-00384-4
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