333 citations · 1.9k across the 64 of their papers we have counts for
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Towards Brain MRI Foundation Models for the Clinic: Findings from the FOMO25 Challenge
Asbjørn Munk, Stefano Cerri, Vardan Nersesjan +81
Clinical deployment of automated brain MRI analysis faces a fundamental challenge: clinical data is heterogeneous and noisy, and high-quality labels are prohibitively costly to obt…
A deep learning model to reduce agent dose for contrast-enhanced MRI of the cerebellopontine angle cistern
Yunjie Chen, Rianne A. Weber, Olaf M. Neve +6
Objectives: To evaluate a deep learning (DL) model for reducing the agent dose of contrast-enhanced T1-weighted MRI (T1ce) of the cerebellopontine angle (CPA) cistern. Materials an…
Consistent View Alignment Improves Foundation Models for 3D Medical Image Segmentation
Puru Vaish, Felix Meister, Tobias Heimann +2
Many recent approaches in representation learning implicitly assume that uncorrelated views of a data point are sufficient to learn meaningful representations for various downstrea…
GReAT: leveraging geometric artery data to improve wall shear stress assessment
Julian Suk, Jolanda J. Wentzel, Patryk Rygiel +3
Leveraging big data for patient care is promising in many medical fields such as cardiovascular health. For example, hemodynamic biomarkers like wall shear stress could be assessed…
Wall Shear Stress Estimation in Abdominal Aortic Aneurysms: Towards Generalisable Neural Surrogate Models
Patryk Rygiel, Julian Suk, Christoph Brune +2
Abdominal aortic aneurysms (AAAs) are pathologic dilatations of the abdominal aorta posing a high fatality risk upon rupture. Studying AAA progression and rupture risk often involv…
Geometric deep learning for local growth prediction on abdominal aortic aneurysm surfaces
Dieuwertje Alblas, Patryk Rygiel, Julian Suk +5
Abdominal aortic aneurysms (AAAs) are progressive focal dilatations of the abdominal aorta. AAAs may rupture, with a survival rate of only 20\%. Current clinical guidelines recomme…
Data-Agnostic Augmentations for Unknown Variations: Out-of-Distribution Generalisation in MRI Segmentation
Puru Vaish, Felix Meister, Tobias Heimann +2
Medical image segmentation models are often trained on curated datasets, leading to performance degradation when deployed in real-world clinical settings due to mismatches between…
Steerable Anatomical Shape Synthesis with Implicit Neural Representations
Bram de Wilde, Max T. Rietberg, Guillaume Lajoinie +1
Generative modeling of anatomical structures plays a crucial role in virtual imaging trials, which allow researchers to perform studies without the costs and constraints inherent t…
Active Learning for Deep Learning-Based Hemodynamic Parameter Estimation
Patryk Rygiel, Julian Suk, Kak Khee Yeung +2
Hemodynamic parameters such as pressure and wall shear stress play an important role in diagnosis, prognosis, and treatment planning in cardiovascular diseases. These parameters ca…
Neural Fields for Continuous Periodic Motion Estimation in 4D Cardiovascular Imaging
Simone Garzia, Patryk Rygiel, Sven Dummer +3
Time-resolved three-dimensional flow MRI (4D flow MRI) provides a unique non-invasive solution to visualize and quantify hemodynamics in blood vessels such as the aortic arch. Howe…
Neural Fields for 3D Tracking of Anatomy and Surgical Instruments in Monocular Laparoscopic Video Clips
Beerend G. A. Gerats, Jelmer M. Wolterink, Seb P. Mol +1
Laparoscopic video tracking primarily focuses on two target types: surgical instruments and anatomy. The former could be used for skill assessment, while the latter is necessary fo…
LaB-GATr: geometric algebra transformers for large biomedical surface and volume meshes
Julian Suk, Baris Imre, Jelmer M. Wolterink
Many anatomical structures can be described by surface or volume meshes. Machine learning is a promising tool to extract information from these 3D models. However, high-fidelity me…
SIRE: scale-invariant, rotation-equivariant estimation of artery orientations using graph neural networks
Dieuwertje Alblas, Julian Suk, Christoph Brune +2
Blood vessel orientation as visualized in 3D medical images is an important descriptor of its geometry that can be used for centerline extraction and subsequent segmentation and vi…
Generative modeling of living cells with SO(3)-equivariant implicit neural representations
David Wiesner, Julian Suk, Sven Dummer +4
Data-driven cell tracking and segmentation methods in biomedical imaging require diverse and information-rich training data. In cases where the number of training samples is limite…
Dynamic Depth-Supervised NeRF for Multi-View RGB-D Operating Room Images
Beerend G. A. Gerats, Jelmer M. Wolterink, Ivo A. M. J. Broeders
The operating room (OR) is an environment of interest for the development of sensing systems, enabling the detection of people, objects, and their semantic relations. Due to freque…
A Comparative Study of Graph Neural Networks for Shape Classification in Neuroimaging
Nairouz Shehata, Wulfie Bain, Ben Glocker
Graph neural networks have emerged as a promising approach for the analysis of non-Euclidean data such as meshes. In medical imaging, mesh-like data plays an important role for mod…
3D Human Pose Estimation in Multi-View Operating Room Videos Using Differentiable Camera Projections
Beerend G. A. Gerats, Jelmer M. Wolterink, Ivo A. M. J. Broeders
3D human pose estimation in multi-view operating room (OR) videos is a relevant asset for person tracking and action recognition. However, the surgical environment makes it challen…
Implicit Neural Representations for Generative Modeling of Living Cell Shapes
David Wiesner, Julian Suk, Sven Dummer +2
Methods allowing the synthesis of realistic cell shapes could help generate training data sets to improve cell tracking and segmentation in biomedical images. Deep generative model…
Direct Automatic Coronary Calcium Scoring in Cardiac and Chest CT
Bob D. de Vos, Jelmer M. Wolterink, Tim Leiner +3
Cardiovascular disease (CVD) is the global leading cause of death. A strong risk factor for CVD events is the amount of coronary artery calcium (CAC). To meet demands of the increa…
Generative adversarial networks and adversarial methods in biomedical image analysis
Jelmer M. Wolterink, Konstantinos Kamnitsas, Christian Ledig +1
Generative adversarial networks (GANs) and other adversarial methods are based on a game-theoretical perspective on joint optimization of two neural networks as players in a game.…
Coronary Artery Centerline Extraction in Cardiac CT Angiography Using a CNN-Based Orientation Classifier
Jelmer M. Wolterink, Robbert W. van Hamersvelt, Max A. Viergever +2
Coronary artery centerline extraction in cardiac CT angiography (CCTA) images is a prerequisite for evaluation of stenoses and atherosclerotic plaque. We propose an algorithm that…
Automatic Segmentation of Thoracic Aorta Segments in Low-Dose Chest CT
Julia M. H. Noothout, Bob D. de Vos, Jelmer M. Wolterink +1
Morphological analysis and identification of pathologies in the aorta are important for cardiovascular diagnosis and risk assessment in patients. Manual annotation is time-consumin…
Improving Myocardium Segmentation in Cardiac CT Angiography using Spectral Information
Steffen Bruns, Jelmer M. Wolterink, Robbert W. van Hamersvelt +3
Accurate segmentation of the left ventricle myocardium in cardiac CT angiography (CCTA) is essential for e.g. the assessment of myocardial perfusion. Automatic deep learning method…
Towards increased trustworthiness of deep learning segmentation methods on cardiac MRI
Jörg Sander, Bob D. de Vos, Jelmer M. Wolterink +1
Current state-of-the-art deep learning segmentation methods have not yet made a broad entrance into the clinical setting in spite of high demand for such automatic methods. One imp…