65 citations · 89 across the 7 of their papers we have counts for
7 papers
MUSTER: Longitudinal Deformable Registration by Composition of Consecutive Deformations
Edvard O. S. Grødem, Donatas Sederevičius, Esten H. Leonardsen +7
Longitudinal imaging allows for the study of structural changes over time. One approach to detecting such changes is by non-linear image registration. This study introduces Multi-S…
Deep Learning-based Intraoperative MRI Reconstruction
Jon André Ottesen, Tryggve Storas, Svein Are Sirirud Vatnehol +8
Purpose: To evaluate the quality of deep learning reconstruction for prospectively accelerated intraoperative magnetic resonance imaging (iMRI) during resective brain tumor surgery…
Treatment-aware Diffusion Probabilistic Model for Longitudinal MRI Generation and Diffuse Glioma Growth Prediction
Qinghui Liu, Elies Fuster-Garcia, Ivar Thokle Hovden +9
Diffuse gliomas are malignant brain tumors that grow widespread through the brain. The complex interactions between neoplastic cells and normal tissue, as well as the treatment-ind…
Computer-Vision Benchmark Segment-Anything Model (SAM) in Medical Images: Accuracy in 12 Datasets
Sheng He, Rina Bao, Jingpeng Li +4
Background: The segment-anything model (SAM), introduced in April 2023, shows promise as a benchmark model and a universal solution to segment various natural images. It comes with…
Voxels Intersecting along Orthogonal Levels Attention U-Net for Intracerebral Haemorrhage Segmentation in Head CT
Qinghui Liu, Bradley J MacIntosh, Till Schellhorn +3
We propose a novel and flexible attention based U-Net architecture referred to as "Voxels-Intersecting Along Orthogonal Levels Attention U-Net" (viola-Unet), for intracranial hemor…
Segmenting white matter hyperintensities on isotropic three-dimensional Fluid Attenuated Inversion Recovery magnetic resonance images: Assessing deep learning tools on norwegian imaging database
Martin Soria Røvang, Per Selnes, Bradley John MacIntosh +5
Automated segmentation of white matter hyperintensities (WMHs) is an essential step in neuroimaging analysis of Magnetic Resonance Imaging (MRI). Fluid Attenuated Inversion Recover…
A Densely Interconnected Network for Deep Learning Accelerated MRI
Jon Andre Ottesen, Matthan W. A. Caan, Inge Rasmus Groote +1
Objective: To improve accelerated MRI reconstruction through a densely connected cascading deep learning reconstruction framework. Materials and Methods: A cascading deep learning…