17 citations · 26 across the 14 of their papers we have counts for
16 papers
Zero-Shot Brain MRI Inpainting with 2.5D Unconditional Flow Priors
Arnela Hadzic, Franz Thaler, Simon Johannes Joham +1
Generative inpainting of brain MRI volumes is essential for synthesizing healthy tissue in pathological regions, improving the accuracy and reliability of automated downstream brai…
The MYOSAIQ Challenge: Myocardial Segmentation with Automated Infarct Quantification
Olivier Bernard, William A. Romero R., Cyprien Bouton +24
Late gadolinium enhancement (LGE) cardiac magnetic resonance (MR) imaging is the modality of choice to assess myocardial infarction (MI) lesions. Nowadays MI volume quantification…
Evaluation of Anatomical Shape Priors in Deep Learning-Based Cardiac Multi-Compartment Segmentation
Michael Hudler, Franz Thaler, Martin Urschler
Whole-heart multi-compartment CT segmentation is clinically important, but standard CNNs do not explicitly enforce anatomical plausibility. Based on statistics derived from the tra…
Semantic-aware Random Convolution and Source Matching for Domain Generalization in Medical Image Segmentation
Franz Thaler, Martin Urschler, Mateusz Kozinski +3
We tackle the challenging problem of single-source domain generalization (DG) for medical image segmentation, where we train a network on one domain (e.g., CT) and directly apply i…
Restora-Flow: Mask-Guided Image Restoration with Flow Matching
Arnela Hadzic, Franz Thaler, Lea Bogensperger +2
Flow matching has emerged as a promising generative approach that addresses the lengthy sampling times associated with state-of-the-art diffusion models and enables a more flexible…
LA-CaRe-CNN: Cascading Refinement CNN for Left Atrial Scar Segmentation
Franz Thaler, Darko Stern, Gernot Plank +1
Atrial fibrillation (AF) represents the most prevalent type of cardiac arrhythmia for which treatment may require patients to undergo ablation therapy. In this surgery cardiac tiss…
Augmentation-based Domain Generalization and Joint Training from Multiple Source Domains for Whole Heart Segmentation
Franz Thaler, Darko Stern, Gernot Plank +1
As the leading cause of death worldwide, cardiovascular diseases motivate the development of more sophisticated methods to analyze the heart and its substructures from medical imag…
An efficient end-to-end computational framework for the generation of ECG calibrated volumetric models of human atrial electrophysiology
Elena Zappon, Luca Azzolin, Matthias A. F. Gsell +12
Computational models of atrial electrophysiology (EP) are increasingly utilized for applications such as the development of advanced mapping systems, personalized clinical therapy…
Integrating anatomy and electrophysiology in the healthy human heart: Insights from biventricular statistical shape analysis using universal coordinates
Lore Van Santvliet, Elena Zappon, Matthias A. F. Gsell +9
A cardiac digital twin is a virtual replica of a patient-specific heart, mimicking its anatomy and physiology. A crucial step of building a cardiac digital twin is anatomical twinn…
Gaussian Process Emulators for Few-Shot Segmentation in Cardiac MRI
Bruno Viti, Franz Thaler, Kathrin Lisa Kapper +3
Segmentation of cardiac magnetic resonance images (MRI) is crucial for the analysis and assessment of cardiac function, helping to diagnose and treat various cardiovascular disease…
Multi-Source and Multi-Sequence Myocardial Pathology Segmentation Using a Cascading Refinement CNN
Franz Thaler, Darko Stern, Gernot Plank +1
Myocardial infarction (MI) is one of the most prevalent cardiovascular diseases and consequently, a major cause for mortality and morbidity worldwide. Accurate assessment of myocar…
pyCEPS: A cross-platform Electroanatomic Mapping Data to Computational Model Conversion Platform for the Calibration of Digital Twin Models of Cardiac Electrophysiology
Robert Arnold, Anton J. Prassl, Aurel Neic +7
Background and Objective: Data from electro-anatomical mapping (EAM) systems are playing an increasingly important role in computational modeling studies for the patient-specific c…
CaRe-CNN: Cascading Refinement CNN for Myocardial Infarct Segmentation with Microvascular Obstructions
Franz Thaler, Matthias A. F. Gsell, Gernot Plank +1
Late gadolinium enhanced (LGE) magnetic resonance (MR) imaging is widely established to assess the viability of myocardial tissue of patients after acute myocardial infarction (MI)…
Biomedical image analysis competitions: The state of current participation practice
Matthias Eisenmann, Annika Reinke, Vivienn Weru +352
The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known abou…
Efficient Multi-Organ Segmentation Using SpatialConfiguration-Net with Low GPU Memory Requirements
Franz Thaler, Christian Payer, Horst Bischof +1
Even though many semantic segmentation methods exist that are able to perform well on many medical datasets, often, they are not designed for direct use in clinical practice. The t…
Modeling Annotation Uncertainty with Gaussian Heatmaps in Landmark Localization
Franz Thaler, Christian Payer, Martin Urschler +1
In landmark localization, due to ambiguities in defining their exact position, landmark annotations may suffer from large observer variabilities, which result in uncertain annotati…