Internship : Statistical Shape Modeling and Deep Learning for 3D Medical Image Segmentation
il y a 7 jours
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**:
- Raju, Ashwin et al. “Deep Implicit Statistical Shape Models for 3D Medical Image Delineation.” _AAAI Conference on Artificial Intelligence_ (2021).
- Wickramasinghe, Udaranga et al. “Voxel2Mesh: 3D Mesh Model Generation from Volumetric Data.” _International Conference on Medical Image Computing and Computer-Assisted Intervention_ (2019).
- 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
- Previous experience with medical image processing is a plus
1+ years
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