Internship : Statistical Shape Modeling and Deep Learning for 3D Medical Image Segmentation
Société : Sim&Cure Lieu : Hérault (Occitanie)
Présentation de l'entreprise
Founded in 2014, Sim&Cure is French digital start-up focused on improving neurovascular treatments of cerebral aneurysm with a proprietary software suite. The Sim&Size™ software is a Class II medical device with CE mark and FDA clearance that has already been used to treat more than 10,000+ patients in 500+ hospitals in the world.
Through several modules, Sim&Size™ is a software suite that provides to the physician a 3D visualization of medical images and a computational model of neurovascular implantable medical devices (IMDs). The therapeutic strategy is the most important step of this disease treatment and Sim&Size™ is now part of it by being efficient, safe, and reproducible. Computational modeling of specific devices in the anatomy of a patient helps the physician to achieve the intended outcome of the intervention.
Descriptif du poste
Société : Sim&Cure Catégorie : Stage Activité : Industrie Filiere : Assurance Metier : Ingénieur d'études et développement Lieu : Hérault (Occitanie) Durée : Stage
Mission
Founded in 2014, Sim&Cure is French digital start-up focused on improving neurovascular treatments of cerebral aneurysm with a proprietary software suite. The Sim&Size™ software is a Class II medical device with CE mark and FDA clearance that has already been used to treat more than 10,000+ patients in 500+ hospitals in the world.
Through several modules, Sim&Size™ is a software suite that provides to the physician a 3D visualization of medical images and a computational model of neurovascular implantable medical devices (IMDs). The therapeutic strategy is the most important step of this disease treatment and Sim&Size™ is now part of it by being efficient, safe, and reproducible. Computational modeling of specific devices in the anatomy of a patient helps the physician to achieve the intended outcome of the intervention.
The internship focuses on improving 3D medical image segmentation accuracy by incorporating statistical prior knowledge into deep learning architectures. The project aims to develop novel methodologies that leverage shape statistics and anatomical constraints to enhance the robustness and reliability of cerebral aneurysm detection and segmentation.
Main Objectives:
* Develop and implement statistical shape models for intracranial aneurysm
* Design and integrate shape priors into deep learning architectures
* Evaluate and validate the proposed methods on medical imaging datasets
Bibliography:
1. Raju, Ashwin et al. “Deep Implicit Statistical Shape Models for 3D Medical Image Delineation.” AAAI Conference on Artificial Intelligence (2021).
2. Wickramasinghe, Udaranga et al. “Voxel2Mesh: 3D Mesh Model Generation from Volumetric Data.” International Conference on Medical Image Computing and Computer-Assisted Intervention (2019).
Profil recherché
* Master's student or final year engineering student in Applied Mathematics, Computer Science, or related field
* Strong mathematical background, particularly in statistics
* Demonstrated experience in deep learning and computer vision
* Practical knowledge of PyTorch
* Programming skills in Python and experience with scientific computing libraries
* Interest in medical imaging and healthcare applications
* Previous experience with medical image processing is a plusCLIQUER ICI POUR POSTULER
