LIFEx is a user-friendly image processing software, making it possible for colleagues without any programming skills to perform radiomic studies using any type of medical images, including PET, MR, CT, SPECT or US images. Software development is performed by Christophe Nioche, a research engineer in LITO, and the software is freely available to the scientific community. The software is constantly evolving to account for the suggestions and contributions of users, with whom we maintain tight interactions. All radiomic features available through this software do conform the recommendations of the international IBSI consortium we belong to.
LIFEx currently has more than 2500 users across the world. Its initial version has been described in a 2008 Cancer Research paper. All you may want to know about LIFEx (download, latest version, FAQ, tutorials) can be found on a dedicated web site.
We are part of the H2020-MSCA-ITN-2017 HYBRID (Healthcare Yearns for Bright Researchers for Imaging Data) project in collaboration with 10 partners from Germany, Austria, United Kingdom, the Netherlands, Danemark, and Belgium. The HYBRID project supports 15 PhD students that are all working towards more powerful in vivo molecular imaging, especially involving PET/MR, for personnalised medicine, using the most advanced technologies and processing approaches, including dynamic imaging and artificial intelligence approaches. To know more about the HYBRID project, please go to the dedicated web site.
VOCALE is a project dedicated to motion analysis of vocal folds using dynamic translaryngeal ultrasound. It is driven by the laboratory in collaboration with surgical departments and the “Laboratoire d’Imagerie Biomédicale”. We developed a dedicated image analysis software and demonstrated its usefulness to detect and quantify vocal fold paralysis on 100 patients with voice disorders after thyroid surgery. A multi-centric clinical trial is under way to validate the use of ultrasound as first-line exam and reduce the number of nasofibroscopies. In this trial, we are combining image analysis methods with deep learning to improve the performance of our detector of vocal fold paralysis associated with a recurrent nerve injury.