48 citations · 159 across the 6 of their papers we have counts for
7 papers
A latent ODE approach to full-cycle biventricular cardiac motion modeling from cine MRI
David Brüggemann, Ekaterina Krymova, Firat Özdemir +6
Cardiac magnetic resonance imaging (CMR) captures rich spatiotemporal information about ventricular structure and motion, but conventional risk models use only a few image-derived…
Delineating Bone Surfaces in B-Mode Images Constrained by Physics of Ultrasound Propagation
Firat Ozdemir, Christine Tanner, Orcun Goksel
Bone surface delineation in ultrasound is of interest due to its potential in diagnosis, surgical planning, and post-operative follow-up in orthopedics, as well as the potential of…
Active Learning for Segmentation Based on Bayesian Sample Queries
Firat Ozdemir, Zixuan Peng, Philipp Fuernstahl +2
Segmentation of anatomical structures is a fundamental image analysis task for many applications in the medical field. Deep learning methods have been shown to perform well, but fo…
Extending Pretrained Segmentation Networks with Additional Anatomical Structures
Firat Ozdemir, Orcun Goksel
Comprehensive surgical planning require complex patient-specific anatomical models. For instance, functional muskuloskeletal simulations necessitate all relevant structures to be s…
Generative Adversarial Networks for MR-CT Deformable Image Registration
Christine Tanner, Firat Ozdemir, Romy Profanter +3
Deformable Image Registration (DIR) of MR and CT images is one of the most challenging registration task, due to the inherent structural differences of the modalities and the missi…
Active Learning for Segmentation by Optimizing Content Information for Maximal Entropy
Firat Ozdemir, Zixuan Peng, Christine Tanner +2
Segmentation is essential for medical image analysis tasks such as intervention planning, therapy guidance, diagnosis, treatment decisions. Deep learning is becoming increasingly p…
Learn the new, keep the old: Extending pretrained models with new anatomy and images
Firat Ozdemir, Philipp Fuernstahl, Orcun Goksel
Deep learning has been widely accepted as a promising solution for medical image segmentation, given a sufficiently large representative dataset of images with corresponding annota…