Using a combination of medical imaging (elasticity imaging using Magnetic Resonance Imaging) and numerical modeling, I highlighted a softening in a muscle component called ExtraCellular Matrix, altering the muscle functionality. The link between medical imaging and numerical modeling? The ITI HealthTech. Thanks to this program, I was provided a high-quality interdisciplinary training as a Master student, as well as funding for both my Master thesis and my PhD thesis, co-directed by ICube in Strasbourg and BMBI in Compiègne. Additionally, HealthTech included scientific events that I could organize and animate as a member of the student board. Overall, I am happy that I took a big turn from my engineering studies to enroll in the HealthTech program and finally became a researcher in biomechanics.

Future plans

In early 2026, I will join the Institut für Rechtsmedizin at LMU in Munich as a postdoctoral researcher. My future works will be conducted in the context of forensic biomechanics and especially accident reconstruction. This position strongly focuses on computational and experimental biomechanics, as well as medical imaging, an interdisciplinary set of competences that I acquired in Strasbourg.

Unfortunately, treating the pelvic region involves injecting cement volumes greater than those used in the vertebrae, which introduces technical challenges and may sometimes require the addition of screws to stabilize critical areas near the hip joint. Despite its growing use, clinical practices remain largely empirical and poorly standardized. Moreover, practitioner training is limited by the absence of dedicated devices specifically designed for learning pelvic cementoplasty.

In this context, my thesis focused on developing several devices and tools to support practitioners:

• A manual injection system adapted to large cement volumes;
• A pelvic phantom for practitioner training;
• The development of patient-specific finite element models to compare different cementoplasty restoration strategies, with and without screws and cement.

Of course, this project would not have been possible without the support of ITI HealthTech, which co-financed my thesis as part of their PhD program, together with the Doctoral School of Mathematics, Information Science, and Engineering (MSII ED 269).

Following my thesis, I would like to continue working in the field of medical device development. I am therefore seeking a postdoctoral position in device development, with a particular interest in the pelvic region, which has been the main anatomical focus of my doctoral work. I wish to continue contributing to innovative clinical solutions by combining experimental work, numerical modeling, and close collaboration with healthcare professionals.

Defense date: 12/11/2025

My PhD thesis investigated a historical and well-known problem in machine learning: what is called the curse of generalization. To make it simpler, if you train a neural network to detect brain tumors on MRI scans acquired at the Strasbourg hospital, and then ask the same neural network to detect tumors on MRIs acquired at a German hospital, its performance will degrade significantly. Why? Because the MRI acquisition settings differ between countries and hospitals. Thus, even if a German and a French scan would look similar to the human eye, the neural network will be lost. This issue arises because of a concept called covariate shift.

My thesis notably brought new theoretical insights regarding the link between covariate shift and modern machine learning methods such as diffusion models (the models that power today's image and video generation tools available on the internet). These theoretical insights were recognized with the best student paper award at the international conference ICASSP in 2025. Beyond the theoretical side, I developed applied methods (all my code is open source on GitHub) for biologists, bringing those theoretical insights to life and enabling them to build more generalizable neural networks to analyze their images.

Where am I now?

I am currently a postdoctoral researcher at the Vrije Universiteit Brussels in Belgium, where I work on the ERC IONIAN project (https://cordis.europa.eu/project/id/101171240), a Consolidator Grant from the European Research Council, with Prof. Nikos Deligiannis. This project focuses on autonomous driving, and more precisely on enabling cars to communicate with each other to enhance their autonomous capabilities. It sits at the crossroads of information theory and computer vision. I work myself on the computer vision side, which consists of point cloud, image, and video processing. I find it very exciting to work in such a multidisciplinary team, especially on a fresh new project that just launched in September 2025... Stay tuned!

To read more about Alexandre and his work, check out the following article in the University magazine Savoir(s): Alexandre Stenger récompensé en Inde pour ses recherches sur l’IA médicale (text available in French only)

To read the award-winning article (in English), check out: Label-constrained unsupervised domain adaptation for semantic segmentation with diffusion models

Both projects are funded through the 2024 joint call between the ITI HealthTech and the IHU Strasbourg.

For more information on the winners, check out the following article published by the INSA Strasbourg: read more about them here.

Abstract

Cardiovascular diseases are one of the leading causes of death worldwide, with an incidence of 17.9 million deaths per year. The primary therapeutic solution is endovascular interventions, which owe their success to significant advantages, such as minimal invasiveness and low costs. However, these procedures are limited by their complexity, requiring extensive clinician training and access to specialized facilities. To deliver treatment, the clinician must navigate long, thin-tube devices, such as catheters and guidewires, through the patient's arteries, controlling them from the proximal end positioned at the entrance of the patient's vessel. This already challenging task is further complicated by the limited guidance the clinician has available, which is only provided by 2D fluoroscopic images. Furthermore, the acquisition of these images requires the use of X-rays, dangerous for the health of both the patient and the clinician, and the visibility of vessels is dependent on contrast agents, which can be harmful to the patient’s kidneys.
To address these limitations, this manuscript proposes solutions to facilitate the intervention, through the development of methods able to offer the clinician more support during certain phases of the intervention while automating others. A simplified procedure can indeed result in lower surgical time and, as a consequence, lower X-ray exposure for both the patient and the caregiver. Two main systems have been developed: one that enhances fluoroscopic images and another that autonomously navigates surgical tools. The first one is essentially an assistance system, which overlaps the classical fluoroscopic images with information about the anatomy that is being navigated and shows the clinicians the predicted outcome of their actions before they perform them. The second one is a Deep Reinforcement Learning controller which aims to autonomously perform the procedure by controlling an endovascular surgical robot. Currently, these robots function only as leader-follower devices, which are not able to provide additional support to the caregiver during the procedure.
In tests conducted on a phantom in the context of a user study, the enhanced fluoroscopic system allowed for a 56% intervention time reduction. The autonomous controller achieved a success rate of over 95%, even when tested on anatomies with characteristics completely different from the training models.

Jury Composition:

Reviewers:

• Pr. Franziska Mathis-Ullrich, University of Erlangen–Nuremberg
• Pr. Emmanuel Vander Poorten, University of KU Leuven

Examiners :

• Pr. Philippe Cattin, University of Basel

Supervisors:

•     - Pr. Stéphane Cotin, Inria Strasbourg, University of Strasbourg
•     - Pr. Florent Nageotte, University of Strasbourg
•     - Pr. Michel Duprez, Inria Strasbourg, University of Strasbourg

Abstract

The objective of this thesis is to develop a new full-field optical coherence tomography (FF-OCT) system for imaging biological tissues in-vivo. This experimental system combines full-field optical coherence tomography (FF-OCT) with an off-axis interferometry technique. With this method, it is possible to simultaneously obtain amplitude and phase images from a single interferogram acquisition. The amplitude images allow for tomographic imaging of biological tissues in vivo with micrometer-level resolution, demonstrating the system’s capability to capture images in in-vivo scenarios.

Direct access to phase images also offers the possibility to incorporate additional contrast modalities, such as elastography. During this thesis, elastography was integrated into the experimental system, enabling the acquisition of stiffness maps of samples. This approach was successfully validated on agarose samples, which mimic the mechanical properties of biological tissues.

Jury Members

• Reviewer: Prof. Arnaud Dubois, Université Paris-Saclay, Charles Fabry Laboratory
• • Reviewer: Prof. Gilles Tessier, Sorbonne University, Vision Institute
• • Examiner: Dr. Serge Miemon, ONERA - Paris Eye Imaging
• • Supervisor: Prof. Nadia Bahlouli, Université de Strasbourg, ICube
• • Co-supervisor: Prof. Sybille Facca, Université de Strasbourg, ICube, University Hospitals of Strasbourg, Hand Surgery Department
• • Advisor: Dr. Amir Na

For more information about our Master's program, check out our Master's training page.

For specific questions that have not been answered on the website, please contact iti-healthtech-master@unistra.fr.

Mr. Abou Hamdan is completing his PhD under the supervision of Siham Touchal (ICube MMB), Hamdi Jmal (ICube MMB), and Delphine Wagner (ICube MMB, Department of Dental Surgery at UNISTRA).

For more information about our Master's program, check out our Master's training page.

For specific questions that have not been answered on the website, please contact iti-healthtech-master@unistra.fr.

Final project attributions will take place during the week of September 23rd and the list of selected projects will be available on our Master's research project page.

His poster deals with the "DEVELOPMENT OF A LOW-COST, 3D-PRINTED PHANTOM FOR DEDICATED 0.31T MRI SCANNER".

This work presents a phantom designed for MRI quality control training. This low-cost phantom, produced by 3D printing, is adaptable to all clinical MRI equipment and meets complex specifications.

His project partners are the ICube UMR 7357 laboratory, Strasbourg, France, and the IHU Strasbourg, Institute of Image-Guided Surgery, Strasbourg, France.

This work of the Interdisciplinary Thematic Institute HealthTech, as part of the ITI 2021-2028 program of the University of Strasbourg, CNRS and Inserm, was supported by IdEx Unistra (ANR-10-IDEX-0002) and SFRI (STRAT’US project, ANR-20-SFRI-0012) under the framework of the French Investments for the Future Program.

see the original release on the ICube newsreel: https://icube.unistra.fr/actualites-agenda/actualites/actualite/?tx_ttnews%5Btt_news%5D=25925&cHash=7a9f8d103edd1d74c0be7c11c1402933

His poster deals with the NUMERICAL STUDY OF HINGE FRACTURE IN DISTAL FEMORAL OSTETOMY BY XFEM.

Distal femoral osteotomy (DFO) is a surgical procedure aimed to correct valgus misalignment of the knee, often accompanied by osteoarthritis of its lateral compartment. However, hinge fracture during OFD can significantly affect postoperative results. The goal of this research project is to evaluate the effectiveness of K-Wires in preventing hinge fracture during the opening phase of OFD. The OFD opening operation was modeled in Abaqus software using the extended finite element method (XFEM).

This work of the Interdisciplinary Thematic Institute HealthTech, as part of the ITI 2021-2028 program of the University of Strasbourg, CNRS and Inserm, was supported by IdEx Unistra (ANR-10-IDEX-0002) and SFRI (STRAT’US project, ANR-20-SFRI-0012) under the framework of the French Investments for the Future Program.

see the original release on the ICube newsreel: https://icube.unistra.fr/actualites-agenda/actualites/actualite/?tx_ttnews%5Btt_news%5D=25927&cHash=65798d707840aab6205be866b517b077