Search Results for author: Franziska Mathis-Ullrich

Found 8 papers, 2 papers with code

Movement Primitive Diffusion: Learning Gentle Robotic Manipulation of Deformable Objects

no code implementations15 Dec 2023 Paul Maria Scheikl, Nicolas Schreiber, Christoph Haas, Niklas Freymuth, Gerhard Neumann, Rudolf Lioutikov, Franziska Mathis-Ullrich

Policy learning in robot-assisted surgery (RAS) lacks data efficient and versatile methods that exhibit the desired motion quality for delicate surgical interventions.

Imitation Learning

Registered and Segmented Deformable Object Reconstruction from a Single View Point Cloud

no code implementations13 Nov 2023 Pit Henrich, Balázs Gyenes, Paul Maria Scheikl, Gerhard Neumann, Franziska Mathis-Ullrich

In deformable object manipulation, we often want to interact with specific segments of an object that are only defined in non-deformed models of the object.

Deformable Object Manipulation Object +1

Grounding Graph Network Simulators using Physical Sensor Observations

1 code implementation23 Feb 2023 Jonas Linkerhägner, Niklas Freymuth, Paul Maria Scheikl, Franziska Mathis-Ullrich, Gerhard Neumann

Our method results in utilization of additional point cloud information to accurately predict stable simulations where existing Graph Network Simulators fail.

Imputation Motion Planning +1

Multimodal CNN Networks for Brain Tumor Segmentation in MRI: A BraTS 2022 Challenge Solution

no code implementations19 Dec 2022 Ramy A. Zeineldin, Mohamed E. Karar, Oliver Burgert, Franziska Mathis-Ullrich

It is worth noting that our ensemble models took first place in the final evaluation on the BraTS testing dataset with Dice scores of 0. 9294, 0. 8788, and 0. 8803, and Hausdorf distance of 5. 23, 13. 54, and 12. 05, for the whole tumor, tumor core, and enhancing tumor, respectively.

Brain Tumor Segmentation Tumor Segmentation

Self-supervised iRegNet for the Registration of Longitudinal Brain MRI of Diffuse Glioma Patients

no code implementations20 Nov 2022 Ramy A. Zeineldin, Mohamed E. Karar, Franziska Mathis-Ullrich, Oliver Burgert

Reliable and accurate registration of patient-specific brain magnetic resonance imaging (MRI) scans containing pathologies is challenging due to tissue appearance changes.

Ensemble CNN Networks for GBM Tumors Segmentation using Multi-parametric MRI

1 code implementation13 Dec 2021 Ramy A. Zeineldin, Mohamed E. Karar, Franziska Mathis-Ullrich, Oliver Burgert

Accurate identification of the malignant brain tumor and its sub-regions is still one of the most challenging problems in medical image segmentation.

Brain Tumor Segmentation Image Segmentation +2

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