Papers
3
Total Citations
46
H-Index
3
About
Marie-Odile Berger is a leading researcher in medical image analysis, computer vision, and augmented reality, with a particular focus on enhancing surgical guidance and robotic assistance. Her most impactful work addresses the challenge of accurately tracking internal anatomical structures during minimally invasive procedures. In her highly cited 2014 paper (21 citations), she introduced a method for tracking liver tumors and vascular networks in robot-assisted surgery, enabling real-time overlay of pre-operative data onto laparoscopic views even under significant tissue deformation. This contribution has been pivotal for advancing image-guided interventions. Berger has also made notable advances in camera pose estimation for augmented reality and robotics. Her 2022 paper (16 citations) and 2020 work (9 citations) propose robust, object-based methods for computing camera pose from ellipsoidal models and 3D-aware ellipse prediction, eliminating the need for detailed scene models. These techniques are critical for deploying AR and robotics in unstructured or challenging environments. With a career spanning over two decades, Berger’s research consistently bridges theoretical computer vision and practical clinical applications, earning her recognition as a key innovator in surgical augmented reality and autonomous navigation systems.
Research Focus
Key Achievements
Top Papers
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- 33D-Aware Ellipse Prediction for Object-Based Camera Pose Estimation9 citations · 2020