Search Results for author: Stefan Zachow

Found 8 papers, 3 papers with code

Landmark-free Statistical Shape Modeling via Neural Flow Deformations

1 code implementation14 Sep 2022 David Lüdke, Tamaz Amiranashvili, Felix Ambellan, Ivan Ezhov, Bjoern Menze, Stefan Zachow

Statistical shape modeling aims at capturing shape variations of an anatomical structure that occur within a given population.

Image Segmentation Semantic Segmentation

Rigid Motion Invariant Statistical Shape Modeling based on Discrete Fundamental Forms

no code implementations5 Nov 2021 Felix Ambellan, Stefan Zachow, Christoph von Tycowicz

shape-based classification of pathological malformations of the human knee and show that it outperforms the standard Euclidean as well as a recent nonlinear approach especially in presence of sparse training data.

Hippocampus Specificity

Geodesic B-Score for Improved Assessment of Knee Osteoarthritis

no code implementations12 Mar 2021 Felix Ambellan, Stefan Zachow, Christoph von Tycowicz

Three-dimensional medical imaging enables detailed understanding of osteoarthritis structural status.

Simulation-to-Real domain adaptation with teacher-student learning for endoscopic instrument segmentation

no code implementations2 Mar 2021 Manish Sahu, Anirban Mukhopadhyay, Stefan Zachow

Conclusion: We show that our proposed approach can successfully exploit the unlabeled real endoscopic video frames and improve generalization performance over pure simulation-based training and the previous state-of-the-art.

Scene Understanding Unsupervised Domain Adaptation

Endo-Sim2Real: Consistency learning-based domain adaptation for instrument segmentation

no code implementations22 Jul 2020 Manish Sahu, Ronja Strömsdörfer, Anirban Mukhopadhyay, Stefan Zachow

Surgical tool segmentation in endoscopic videos is an important component of computer assisted interventions systems.

Domain Adaptation

Tool and Phase recognition using contextual CNN features

no code implementations27 Oct 2016 Manish Sahu, Anirban Mukhopadhyay, Angelika Szengel, Stefan Zachow

A transfer learning method for generating features suitable for surgical tools and phase recognition from the ImageNet classification features [1] is proposed here.

Classification General Classification +3

Shape-aware Surface Reconstruction from Sparse 3D Point-Clouds

1 code implementation26 Feb 2016 Florian Bernard, Luis Salamanca, Johan Thunberg, Alexander Tack, Dennis Jentsch, Hans Lamecker, Stefan Zachow, Frank Hertel, Jorge Goncalves, Peter Gemmar

Estimating the parameters of the GMM in a maximum a posteriori manner yields the reconstruction of the surface from the given data points.

Anatomy Surface Reconstruction

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