103 citations · 237 across the 8 of their papers we have counts for
9 papers
Blood vessel segmentation in en-face OCTA images: a frequency based method
Anna Breger, Felix Goldbach, Bianca S. Gerendas +2
Optical coherence tomography angiography (OCTA) is a novel noninvasive imaging modality for visualization of retinal blood flow in the human retina. Using specific OCTA imaging bio…
An amplified-target loss approach for photoreceptor layer segmentation in pathological OCT scans
José Ignacio Orlando, Anna Breger, Hrvoje Bogunović +4
Segmenting anatomical structures such as the photoreceptor layer in retinal optical coherence tomography (OCT) scans is challenging in pathological scenarios. Supervised deep learn…
Using CycleGANs for effectively reducing image variability across OCT devices and improving retinal fluid segmentation
Philipp Seeböck, David Romo-Bucheli, Sebastian Waldstein +5
Optical coherence tomography (OCT) has become the most important imaging modality in ophthalmology. A substantial amount of research has recently been devoted to the development of…
U2-Net: A Bayesian U-Net model with epistemic uncertainty feedback for photoreceptor layer segmentation in pathological OCT scans
José Ignacio Orlando, Philipp Seeböck, Hrvoje Bogunović +5
In this paper, we introduce a Bayesian deep learning based model for segmenting the photoreceptor layer in pathological OCT scans. Our architecture provides accurate segmentations…
On orthogonal projections for dimension reduction and applications in augmented target loss functions for learning problems
Anna Breger, Jose Ignacio Orlando, Pavol Harar +6
The use of orthogonal projections on high-dimensional input and target data in learning frameworks is studied. First, we investigate the relations between two standard objectives i…
Unsupervised Identification of Disease Marker Candidates in Retinal OCT Imaging Data
Philipp Seeböck, Sebastian M. Waldstein, Sophie Klimscha +6
The identification and quantification of markers in medical images is critical for diagnosis, prognosis, and disease management. Supervised machine learning enables the detection a…
Fully Automated Segmentation of Hyperreflective Foci in Optical Coherence Tomography Images
Thomas Schlegl, Hrvoje Bogunovic, Sophie Klimscha +6
The automatic detection of disease related entities in retinal imaging data is relevant for disease- and treatment monitoring. It enables the quantitative assessment of large amoun…
Reproducibility of Retinal Thickness Measurements across Spectral-Domain Optical Coherence Tomography Devices using Iowa Reference Algorithm
Adnan Rashid, Sebastian M. Waldstein, Bianca S. Gerendas +8
PURPOSE: Establishing and obtaining consistent quantitative indices of retinal thickness from a variety of clinically used Spectral-Domain Optical Coherence Tomography scanners. DE…
Identifying and Categorizing Anomalies in Retinal Imaging Data
Philipp Seeböck, Sebastian Waldstein, Sophie Klimscha +5
The identification and quantification of markers in medical images is critical for diagnosis, prognosis and management of patients in clinical practice. Supervised- or weakly super…