215 citations · 217 across the 6 of their papers we have counts for
6 papers
Algorithmic statistics of retinal images
Loan Huynh, Ronald Zambrano, Layton Aho +4
There has been a tremendous amount of image processing and machine learning research to measure and classify disease progression from live optical coherence tomography (OCT) imagin…
Self-supervised OCT Image Denoising with Slice-to-Slice Registration and Reconstruction
Shijie Li, Palaiologos Alexopoulos, Anse Vellappally +3
Strong speckle noise is inherent to optical coherence tomography (OCT) imaging and represents a significant obstacle for accurate quantitative analysis of retinal structures which…
Uncertainty guided semi-supervised segmentation of retinal layers in OCT images
Suman Sedai, Bhavna Antony, Ravneet Rai +5
Deep convolutional neural networks have shown outstanding performance in medical image segmentation tasks. The usual problem when training supervised deep learning methods is the l…
Dueling Deep Q-Network for Unsupervised Inter-frame Eye Movement Correction in Optical Coherence Tomography Volumes
Yasmeen M. George, Suman Sedai, Bhavna J. Antony +4
In optical coherence tomography (OCT) volumes of retina, the sequential acquisition of the individual slices makes this modality prone to motion artifacts, misalignments between ad…
Inference of visual field test performance from OCT volumes using deep learning
Stefan Maetschke, Bhavna Antony, Hiroshi Ishikawa +3
Visual field tests (VFT) are pivotal for glaucoma diagnosis and conducted regularly to monitor disease progression. Here we address the question to what degree aggregate VFT measur…
A feature agnostic approach for glaucoma detection in OCT volumes
Stefan Maetschke, Bhavna Antony, Hiroshi Ishikawa +3
Optical coherence tomography (OCT) based measurements of retinal layer thickness, such as the retinal nerve fibre layer (RNFL) and the ganglion cell with inner plexiform layer (GCI…