LICENSE
ODIASP is an open-source software that calculates muscle surface area in the L3 slice of CT scans, strictly within the scope of research not involving human subjects. ODIASP V2.2 Copyright (C) 2024 <Dr. Bétry C, Dumont C, Charrière K, Ragusa A> CHUGA/TIMC
This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License or (at your option) any later version.
This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.
You should have received a copy of the GNU General Public License along with this program; if not, see < http://www.gnu.org/licenses>.
Terms of Use
ODIASP is a software tool dedicated to research that does not involve human subjects and must not be used for clinical care. ODIASP is a registered trademark. Access to the software does not grant the right to use the ODIASP name beyond referring to its use in the context of GNU GPLv3 license distribution.
If you use ODIASP, please cite the following publications:
ODIASP: An Open User-Friendly Software for Automated SMI Determination—Application to an Inpatient Population.
medRxiv
https://doi.org/10.1101/2024.10.25.24316094
K. Charrière, A. Ragusa, B. Genoux, A. Vilotitch, S. Artemova, P.A. Beaudoin, P.E. Madiot, G. Ferretti, I. Bricault, J.L. Bosson, E. Fontaine, A. Moreau-Gaudry, J. Giai, C. Bétry
ODIASP integrates open-source code elements. Please cite these as well:
Population-Scale CT-Based Body Composition Analysis Of a Large Outpatient Population Using Deep Learning To Derive Age, Sex, and Race-Specific Reference Curves.
Radiology 298 (2): 319-29
https://doi.org/10.1148/radiol.2020201640
GitHub: CT Body Composition
K. Magudia, C.P. Bridge, C.P. Bay, A. Babic, F.J. Fintelmann, F. Troschel, N. Miskin, W. Wrobel, L.K. Brais, K.P. Andriole, B.M. Wolpin, M.H. Rosenthal
Fully-Automated Analysis of Body Composition from CT in Cancer Patients Using Convolutional Neural Networks.
In , 11041:204-13
https://doi.org/10.1007/978-3-030-01201-4_22
GitHub: CT Body Composition
C.P. Bridge, M. Rosenthal, B. Wright, G. Kotecha, F. Fintelmann, F. Troschel, N. Miskin, K. Desai, W. Wrobel, A. Babic, N. Khalaf, L. Brais, M. Welch, C. Zellers, N. Tenenholtz, M. Michalski, B. Wolpin, K. Andriole
Automated body composition analysis of clinically acquired computed tomography scans using neural networks.
Clinical Nutrition 39 (10): 3049-55
https://doi.org/10.1016/j.clnu.2020.01.008
M.T. Paris, P. Tandon, D.K. Heyland, H. Furberg, T. Premji, G. Low, M. Mourtzakis
ODIASP 2024