48 citations · 108 across the 5 of their papers we have counts for
6 papers
Three-Dimensional Automated Assessment of the Distal Radioulnar Joint Morphology according to Sigmoid Notch Surface Orientation
Simon Roner, Philipp Fürnstahl, Anne-Gita Scheibler +2
The aim of this study was to develop a new method for generating reproducible 3D measurements for the quantification of the distal radioulnar joint morphology. We hypothesized that…
Automatic Modelling of Human Musculoskeletal Ligaments -- Framework Overview and Model Quality Evaluation
Noura Hamze, Lukas Nocker, Nikolaus Rauch +4
Accurate segmentation of connective soft tissues is still a challenging task, which hinders the generation of corresponding geometric models for biomechanical computations. Alterna…
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…
An Automatic Genetic Algorithm Framework for the Optimization of Three-dimensional Surgical Plans of Forearm Corrective Osteotomies
Fabio Carrillo, Simon Roner, Marco von Atzigen +5
3D computer-assisted corrective osteotomy has become the state-of-the-art for surgical treatment of complex bone deformities. Despite available technologies, the automatic generati…
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…