35 citations · 81 across the 5 of their papers we have counts for
5 papers
SemAug: Semantically Meaningful Image Augmentations for Object Detection Through Language Grounding
Morgan Heisler, Amin Banitalebi-Dehkordi, Yong Zhang
Data augmentation is an essential technique in improving the generalization of deep neural networks. The majority of existing image-domain augmentations either rely on geometric an…
Microvasculature Segmentation and Inter-capillary Area Quantification of the Deep Vascular Complex using Transfer Learning
Julian Lo, Morgan Heisler, Vinicius Vanzan +6
Purpose: Optical Coherence Tomography Angiography (OCT-A) permits visualization of the changes to the retinal circulation due to diabetic retinopathy (DR), a microvascular complica…
Cascaded Deep Neural Networks for Retinal Layer Segmentation of Optical Coherence Tomography with Fluid Presence
Donghuan Lu, Morgan Heisler, Da Ma +5
Optical coherence tomography (OCT) is a non-invasive imaging technology which can provide micrometer-resolution cross-sectional images of the inner structures of the eye. It is wid…
Deep learning vessel segmentation and quantification of the foveal avascular zone using commercial and prototype OCT-A platforms
Morgan Heisler, Forson Chan, Zaid Mammo +15
Automatic quantification of perifoveal vessel densities in optical coherence tomography angiography (OCT-A) images face challenges such as variable intra- and inter-image signal to…
Retinal Fluid Segmentation and Detection in Optical Coherence Tomography Images using Fully Convolutional Neural Network
Donghuan Lu, Morgan Heisler, Sieun Lee +3
As a non-invasive imaging modality, optical coherence tomography (OCT) can provide micrometer-resolution 3D images of retinal structures. Therefore it is commonly used in the diagn…