Marie‐Luise Wille
Papers
2
Total Citations
35
H-Index
2
About
Marie-Luise Wille is pioneering the integration of advanced ultrasound imaging with robotic surgery, focusing on knee arthroscopy. Her core research areas include 4D ultrasound imaging, Bayesian deep learning for medical image segmentation, and the creation of dynamic anatomical atlases for autonomous surgical guidance. Wille’s major contributions lie in demonstrating the feasibility of using high-refresh-rate 3D (4D) ultrasound to construct a volumetric atlas of the knee’s anterior compartment, a critical step toward autonomous robotic platforms. She also developed a Bayesian convolutional neural network to quantify segmentation uncertainty in 4D ultrasound images of femoral cartilage, directly addressing the inherent challenges of ultrasound—such as inhomogeneous intensity and boundary variability—to improve surgical guidance. Her most cited works, including a 2020 study on Bayesian CNN segmentation (20 citations) and a 2020 feasibility study on 4D ultrasound-based knee joint atlases (15 citations), have laid foundational groundwork for safer, more precise robotic knee arthroscopy. By tackling the complexities of real-time ultrasound imaging, Wille is helping to transform arthroscopic surgery from a manually guided procedure into a data-driven, automated process.
Research Focus
Key Achievements
Top Papers
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- 2