30 citations · 38 across the 10 of their papers we have counts for
13 papers
Using Foundation Models as Pseudo-Label Generators for Pre-Clinical 4D Cardiac CT Segmentation
Anne-Marie Rickmann, Stephanie L. Thorn, Shawn S. Ahn +9
Cardiac image segmentation is an important step in many cardiac image analysis and modeling tasks such as motion tracking or simulations of cardiac mechanics. While deep learning h…
Progressive Test Time Energy Adaptation for Medical Image Segmentation
Xiaoran Zhang, Byung-Woo Hong, Hyoungseob Park +5
We propose a model-agnostic, progressive test-time energy adaptation approach for medical image segmentation. Maintaining model performance across diverse medical datasets is chall…
V2C-Long: Longitudinal Cortex Reconstruction with Spatiotemporal Correspondence
Fabian Bongratz, Jan Fecht, Anne-Marie Rickmann +1
Reconstructing the cortex from longitudinal magnetic resonance imaging (MRI) is indispensable for analyzing morphological alterations in the human brain. Despite the recent advance…
Neural deformation fields for template-based reconstruction of cortical surfaces from MRI
Fabian Bongratz, Anne-Marie Rickmann, Christian Wachinger
The reconstruction of cortical surfaces is a prerequisite for quantitative analyses of the cerebral cortex in magnetic resonance imaging (MRI). Existing segmentation-based methods…
Keep the Faith: Faithful Explanations in Convolutional Neural Networks for Case-Based Reasoning
Tom Nuno Wolf, Fabian Bongratz, Anne-Marie Rickmann +2
Explaining predictions of black-box neural networks is crucial when applied to decision-critical tasks. Thus, attribution maps are commonly used to identify important image regions…
Abdominal organ segmentation via deep diffeomorphic mesh deformations
Fabian Bongratz, Anne-Marie Rickmann, Christian Wachinger
Abdominal organ segmentation from CT and MRI is an essential prerequisite for surgical planning and computer-aided navigation systems. It is challenging due to the high variability…