ODIASP

A simple and intuitive tool

Select a slice at the level of the third lumbar vertebra (L3)

Import your abdominal CT scan images in DICOM format. The ODIASP tool will automatically identify a slice at the level of the third lumbar vertebra (L3 slice).

Segment the muscle

The ODIASP tool segments the skeletal muscles present in the selected L3 slice from the first step.

Visualize and validate results

The ODIASP tool allows you to easily visualize the steps leading to the calculation of the muscle surface index. Manual validation can be kept for each subject if necessary.

WATCH THE DEMO →

Download the software

ODIASP is an open-source software dedicated to research that does not involve human subjects and must not be used in clinical care.
To obtain the download link, we kindly ask you to fill out the following form. This form also helps us better understand ODIASP users and its potential applications.

To obtain the download link, we kindly ask you to fill out the following form.
This form also helps us better understand ODIASP users and its potential applications.

Last name

First name

Company/ institution

Position

What are your reasons for using ODIASP ?

How long ?

How did you hear about ODIASP ?

Publications

Congress

Other

Email *

Do you agree to be contacted by the ODIASP team (new version updates and news)? *

Yes

I consent to the collection and processing of my personal data submitted through this form, in accordance with the data processing policy of the University Hospital Grenoble. *
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Available for Windows OS

Version 2.2.9

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