164 citations
- University of CopenhagenDK7 papers
- Technical University of DenmarkDK6 papers
- Hvidovre HospitalDK5 papers
- Copenhagen University HospitalDK2 papers
- Erasmus MCNL2 papers
- University of KentGB2 papers
- Athinoula A. Martinos Center for Biomedical ImagingUS1 paper
- Eindhoven University of TechnologyNL1 paper
- Erasmus University RotterdamNL1 paper
- Harvard UniversityUS1 paper
- Massachusetts General HospitalUS1 paper
- Ørsted (Denmark)DK1 paper
9 papers
Shot-noise limited, supercontinuum based optical coherence tomography
Shreesha Rao D. S., Mikkel Jensen, Lars Grüner-Nielsen +8
We present the first demonstration of shot-noise limited supercontinuum-based spectral domain optical coherence tomography (SD-OCT) with axial resolution of 5.9 m at a center wa…
A Contrast-Adaptive Method for Simultaneous Whole-Brain and Lesion Segmentation in Multiple Sclerosis
Stefano Cerri, Oula Puonti, Dominik S. Meier +4
Here we present a method for the simultaneous segmentation of white matter lesions and normal-appearing neuroanatomical structures from multi-contrast brain MRI scans of multiple s…
Accessibility of Cortical Regions to Focal TES: Dependence on Spatial Position, Safety, and Practical Constraints
Guilherme Bicalho Saturnino, Hartwig Roman Siebner, Axel Thielscher +1
Transcranial electric stimulation (TES) can modulate intrinsic neural activity in the brain by injecting weak currents through electrodes attached to the scalp. TES has been widely…
Learning to quantify emphysema extent: What labels do we need?
Silas Nyboe Ørting, Jens Petersen, Laura H. Thomsen +2
Accurate assessment of pulmonary emphysema is crucial to assess disease severity and subtype, to monitor disease progression and to predict lung cancer risk. However, visual assess…
Deep Learning from Label Proportions for Emphysema Quantification
Gerda Bortsova, Florian Dubost, Silas Ørting +5
We propose an end-to-end deep learning method that learns to estimate emphysema extent from proportions of the diseased tissue. These proportions were visually estimated by experts…
Feature learning based on visual similarity triplets in medical image analysis: A case study of emphysema in chest CT scans
Silas Nyboe Ørting, Jens Petersen, Veronika Cheplygina +3
Supervised feature learning using convolutional neural networks (CNNs) can provide concise and disease relevant representations of medical images. However, training CNNs requires a…