A5046488947- Surgical Simulation and Training
- Anatomy and Medical Technology
- Cardiac, Anesthesia and Surgical Outcomes
- Shoulder Injury and Treatment
- Musculoskeletal pain and rehabilitation
- Augmented Reality Applications
- Simulation-Based Education in Healthcare
- Time Series Analysis and Forecasting
- Digital Imaging in Medicine
- Hemodynamic Monitoring and Therapy
- Healthcare Operations and Scheduling Optimization
- Artificial Intelligence in Healthcare and Education
- Medical Imaging and Analysis
- Patient Safety and Medication Errors
- Minimally Invasive Surgical Techniques
- Prenatal Screening and Diagnostics
- Gastrointestinal Bleeding Diagnosis and Treatment
- Soft Robotics and Applications
- Topic Modeling
- 3D Shape Modeling and Analysis
- Organ Donation and Transplantation
- Pregnancy and preeclampsia studies
- Scientific Computing and Data Management
- Enhanced Recovery After Surgery
- Maternal and Perinatal Health Interventions
Laboratoire Traitement du Signal et de l'Image
2018-2025
Université de Rennes
2017-2025
Inserm
2016-2025
Centre Hospitalier Universitaire de Rennes
2023
Translational Innovation in Medicine and Complexity
2017-2020
Centre National de la Recherche Scientifique
2017
Université Grenoble Alpes
2017
The number of international benchmarking competitions is steadily increasing in various fields machine learning (ML) research and practice. So far, however, little known about the common practice as well bottlenecks faced by community tackling questions posed. To shed light on status quo algorithm development specific field biomedical imaging analysis, we designed an survey that was issued to all participants challenges conducted conjunction with IEEE ISBI 2021 MICCAI conferences (80 total)....
Abstract Purpose Limited data exist on the actual transfer of skills learned using a virtual reality (VR) simulator for arthroscopy training because studies mainly focused VR performance improvement and not to real word (transfer validity). The purpose this single‐blinded, controlled trial was objectively investigate validity in context initial knee training. Methods For study, 36 junior resident orthopaedic surgeons (postgraduate year one two) without prior experience arthroscopic surgery...
Background and Objective: Context-aware computer-assisted surgical systems require real-time automatic accurate workflow recognition. Since several years, video has been the most commonly used modality to develop recognition systems. With democratization of robotic-assisted surgery segmentation methods, new modalities are now accessible, such as kinematics. Some previous works these input their models, but added value rarely studied. This paper presents design results 'PEg TRAnsfer Workflow...
This paper presents the design and results of "PEg TRAnsfert Workflow recognition" (PETRAW) challenge whose objective was to develop surgical workflow recognition methods based on one or several modalities, among video, kinematic, segmentation data, in order study their added value. The PETRAW provided a data set 150 peg transfer sequences performed virtual simulator. composed videos, kinematics, semantic segmentation, annotations which described at three different granularity levels: phase,...
Improving surgical training by means of technology assistance is an important challenge that aims to directly impact quality. Surgical includes the acquisition two categories knowledge: declarative knowledge (i.e. 'knowing what') and procedural how'). It essential acquire both before performing any particular surgery. There are currently many tools for acquiring knowledge, such as simulators. However, few approaches or allow a trainer formalize record trainee have easy access it. In this...