3 citations · 3 across the 5 of their papers we have counts for
6 papers · 1 filter
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…
Siamese Networks with Location Prior for Landmark Tracking in Liver Ultrasound Sequences
Alvaro Gomariz, Weiye Li, Ece Ozkan +2
Image-guided radiation therapy can benefit from accurate motion tracking by ultrasound imaging, in order to minimize treatment margins and radiate moving anatomical targets, e.g.,…
Iterative Interaction Training for Segmentation Editing Networks
Gustav Bredell, Christine Tanner, Ender Konukoglu
Automatic segmentation has great potential to facilitate morphological measurements while simultaneously increasing efficiency. Nevertheless often users want to edit the segmentati…
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…