Le département Surgical Data Science (SDS) a pour but d’améliorer les résultats de la chirurgie grâce à des systèmes logiciels basés sur l’IA et pilotés par des données cliniques. La mission de notre équipe est de créer des systèmes innovants qui améliorent les capacités des équipes chirurgicales grâce à l’IA, pour réduire les complications, démocratiser la chirurgie et obtenir de meilleurs résultats pour les patients.
L’échographie est une technologie clé pour détecter le cancer de l’abdomen tôt et le traiter avec une intervention minimale. Le projet disrumpere vise à combiner des appareils à ultrasons à faible coût avec des technologies innovantes d’IA et de robotique, afin de rendre les échographie plus faciles, plus rapides et plus largement utilisés.
Nous créons des systèmes informatiques pour améliorer la chirurgie laparoscopique grâce à la technologie de la réalité augmentée (RA). Il s’agit de fusionner des données d’images médicales en 3D, comme la tomographie ou l’imagerie par résonance magnétique, avec la vidéo laparoscopique, afin de montrer en temps réel l’emplacement des structures critiques telles que les tumeurs et les vaisseaux principaux. La réalité augmentée peut améliorer la sécurité, réduire la durée des opérations, simplifier les procédures complexes et rendre la chirurgie plus accessible dans les pays en développement.
La chirurgie percutanée est une technique très peu invasive permettant de biopsier et de traiter des lésions à l’aide de petites aiguilles passées à travers la peau. Nous créons des systèmes informatiques pour rendre la chirurgie percutanée plus facile, plus sûre, moins dépendante de l’opérateur, et pour étendre son utilisation aux tumeurs précoces et avancées, en utilisant une combinaison d’algorithmes logiciels innovants et de technologies de suivi des instruments en 3D.
Objective skill assessment is becoming an increasingly important component of surgery education and high-stakes skill assessment for accreditation. Our goal is to combine low-cost mechanical simulators with AI to make these tools broadly accessible.
Sight, the Surgical Image Guidance and Healthcare Toolkit facilitates the creation of software based on medical imaging.
It includes various features such as 2D and 3D medical image processing (CT/MRI/US), video processing, visualization, augmented reality, and connectivity with tracking systems. It can be used to write navigation systems, simulators, planning software, or even simple video filtering applications.
Sight is written in C++ and built on top of the best open-source libraries in the field such as OpenCV, ITK, VTK, PCL, and Qt and makes their usage easier by providing data common formats and wrappers. It is based on a modular object/service architecture, making building software application as simple as connecting together data, algorithms and user interface. It runs on Windows and Linux and is freely available under the LGPL.
Medical image and segmentation viewer. It supports many popular formats including DICOM and VTK.
User-friendly application to calibrate mono and stereo cameras. Very handy since camera calibration is a prerequisite in any AR application.
This dataset is composed of the CT-scans of 10 women and 10 men with hepatic tumors in 75% of cases.
Where appropriate, the Couinaud segment number corresponding to the location of tumors is also provided.
This dataset is composed of 2 anonymized CT-scans.
The first one has been realized during the arterial phase in inhaled position, whereas the second one has been realized during the portal phase in exhaled position.
The patient has a hepatic focal nodular hyperplasia in segment VII according to Couinaud’s description.
DePoLL (the Deformable Porcine Laparoscopic Liver) dataset was created to quantitatively evaluate registration accuracy for AR-guided liver surgery using a pre-operative CT model.
Josiane UWINEZA
Research Engineer
Python, Data science, Machine Learning, Deep Learning, Computer Vision, Prayer
Didier WECKMANN
Senior Software Developer
C++, Python, JavaScript, Software Architecture, Continuous Integration, Science Fanboy
Flavio MILANA
Fellow
Cyriaque ZIRIMWABAGABO
Research Engineer
Yvonne KEEZA
Medical Imaging Annotator
Medical Image Data Analysis, Radiology, Ultrasound, Project Management, Image Protocol and Annotations, Medical Technology Enthusiast, Salsa Dancer
Jean De Dieu NIYONTEZE
Research Engineer
Grace UFITINEMA
Medical Imaging Annotator
Medical Image Analysis, Radiography, Ultrasound, Annotation, Basketball-Lover
Luis MENDOZA
Senior Software Developer
Qt, C++, Computer Vision, Another guitar-playing, Football-loving latino
Baptiste PODVIN
Phd. Student
Shamim SEDGHI
Master Student
Nicolas PAPIER
Senior Software Developer
Florien UJEMURWEGO
Medical Imaging Annotator
Medical Image Analysis, Radiography, Ultrasound, Annotation, Medical Image Management, Swimming-Love
Güinther SAIBRO
Research Engineer
Python, Deep Learning, Statistics, Medical Image Analysis, Ultrasound, Cycling
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