1 citations · 1 across the 2 of their papers we have counts for
5 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…
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…
Flow Matching for Conditional MRI-CT and CBCT-CT Image Synthesis
Arnela Hadzic, Simon Johannes Joham, Martin Urschler
Generating synthetic CT (sCT) from MRI or CBCT plays a crucial role in enabling MRI-only and CBCT-based adaptive radiotherapy, improving treatment precision while reducing patient…
Synthetic Augmentation for Anatomical Landmark Localization using DDPMs
Arnela Hadzic, Lea Bogensperger, Simon Johannes Joham +1
Deep learning techniques for anatomical landmark localization (ALL) have shown great success, but their reliance on large annotated datasets remains a problem due to the tedious an…
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…