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…
HALOS: Hallucination-free Organ Segmentation after Organ Resection Surgery
Anne-Marie Rickmann, Murong Xu, Tom Nuno Wolf +2
The wide range of research in deep learning-based medical image segmentation pushed the boundaries in a multitude of applications. A clinically relevant problem that received less…
Joint Reconstruction and Parcellation of Cortical Surfaces
Anne-Marie Rickmann, Fabian Bongratz, Sebastian Pölsterl +2
The reconstruction of cerebral cortex surfaces from brain MRI scans is instrumental for the analysis of brain morphology and the detection of cortical thinning in neurodegenerative…
Vox2Cortex: Fast Explicit Reconstruction of Cortical Surfaces from 3D MRI Scans with Geometric Deep Neural Networks
Fabian Bongratz, Anne-Marie Rickmann, Sebastian Pölsterl +1
The reconstruction of cortical surfaces from brain magnetic resonance imaging (MRI) scans is essential for quantitative analyses of cortical thickness and sulcal morphology. Althou…
STRUDEL: Self-Training with Uncertainty Dependent Label Refinement across Domains
Fabian Gröger, Anne-Marie Rickmann, Christian Wachinger
We propose an unsupervised domain adaptation (UDA) approach for white matter hyperintensity (WMH) segmentation, which uses Self-Training with Uncertainty DEpendent Label refinement…
Importance Driven Continual Learning for Segmentation Across Domains
Sinan Özgür Özgün, Anne-Marie Rickmann, Abhijit Guha Roy +1
The ability of neural networks to continuously learn and adapt to new tasks while retaining prior knowledge is crucial for many applications. However, current neural networks tend…
Recalibrating 3D ConvNets with Project & Excite
Anne-Marie Rickmann, Abhijit Guha Roy, Ignacio Sarasua +1
Fully Convolutional Neural Networks (F-CNNs) achieve state-of-the-art performance for segmentation tasks in computer vision and medical imaging. Recently, computational blocks term…
`Project & Excite' Modules for Segmentation of Volumetric Medical Scans
Anne-Marie Rickmann, Abhijit Guha Roy, Ignacio Sarasua +2
Fully Convolutional Neural Networks (F-CNNs) achieve state-of-the-art performance for image segmentation in medical imaging. Recently, squeeze and excitation (SE) modules and varia…