activity
20182025
most citedEvaluation of Algorithms for Multi-Modality Whole Heart Segmentation: An Open-Access Grand Challenge

29 citations · 36 across the 8 of their papers we have counts for

collaborators
Showing eess.IVShow all

6 papers · 1 filter

eess.IV20251 cited

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…

eess.IV2024

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…

eess.IV2023

Teeth Localization and Lesion Segmentation in CBCT Images using SpatialConfiguration-Net and U-Net

Arnela Hadzic, Barbara Kirnbauer, Darko Stern +1

The localization of teeth and segmentation of periapical lesions in cone-beam computed tomography (CBCT) images are crucial tasks for clinical diagnosis and treatment planning, whi…

eess.IV2020

Inferring the 3D Standing Spine Posture from 2D Radiographs

Amirhossein Bayat, Anjany Sekuboyina, Johannes C. Paetzold +5

The treatment of degenerative spinal disorders requires an understanding of the individual spinal anatomy and curvature in 3D. An upright spinal pose (i.e. standing) under natural…

eess.IV20203 cited

Variational Inference and Bayesian CNNs for Uncertainty Estimation in Multi-Factorial Bone Age Prediction

Stefan Eggenreich, Christian Payer, Martin Urschler +1

Additionally to the extensive use in clinical medicine, biological age (BA) in legal medicine is used to assess unknown chronological age (CA) in applications where identification…

eess.IV2019

Integrating Spatial Configuration into Heatmap Regression Based CNNs for Landmark Localization

Christian Payer, Darko Štern, Horst Bischof +1

In many medical image analysis applications, often only a limited amount of training data is available, which makes training of convolutional neural networks (CNNs) challenging. In…