Lena Maier-Hein

TL;DR: Robust targeting precision of this order of magnitude would significantly improve the clinical treatment standard for various CT-guided minimally invasive interventions in the liver.

TL;DR: The Medical-Out-Of-Distribution-Analysis-Challenge (MOOD) is introduced as an open, fair, and unbiased benchmark for OoD methods in the medical imaging domain and shows that performance has a strong positive correlation with the perceived difficulty, and that all algorithms show a high variance for different anomalies, making it yet hard to recommend them for clinical practice.

TL;DR: A new approach to on-patient visualization of 3D medical images, which combines the concept of augmented reality (AR) with an intuitive interaction scheme is presented, which can be used for intervention planning, anatomy teaching and various other applications that require intuitive visualization of3D data.

TL;DR: The robustness of a previously introduced motion compensation approach was increased by taking into account the already traveled trajectory of the instrument within the lung and a virtual environment, which accounts for respiratory motion and electromagnetic noise was used.