11 citations · 17 across the 11 of their papers we have counts for
11 papers
OCTolyzer: Fully automatic toolkit for segmentation and feature extracting in optical coherence tomography and scanning laser ophthalmoscopy data
Jamie Burke, Justin Engelmann, Samuel Gibbon +9
Optical coherence tomography (OCT) and scanning laser ophthalmoscopy (SLO) of the eye has become essential to ophthalmology and the emerging field of oculomics, thus requiring a ne…
SLOctolyzer: Fully automatic analysis toolkit for segmentation and feature extracting in scanning laser ophthalmoscopy images
Jamie Burke, Samuel Gibbon, Justin Engelmann +6
Purpose: The purpose of this study was to introduce SLOctolyzer: an open-source analysis toolkit for en face retinal vessels in infrared reflectance scanning laser ophthalmoscopy (…
Domain-specific augmentations with resolution agnostic self-attention mechanism improves choroid segmentation in optical coherence tomography images
Jamie Burke, Justin Engelmann, Charlene Hamid +9
The choroid is a key vascular layer of the eye, supplying oxygen to the retinal photoreceptors. Non-invasive enhanced depth imaging optical coherence tomography (EDI-OCT) has recen…
Training a high-performance retinal foundation model with half-the-data and 400 times less compute
Justin Engelmann, Miguel O. Bernabeu
Artificial Intelligence in medicine is traditionally limited by the lack of massive training datasets. Foundation models, pre-trained models that can be adapted to downstream tasks…
Applicability of oculomics for individual risk prediction: Repeatability and robustness of retinal Fractal Dimension using DART and AutoMorph
Justin Engelmann, Diana Moukaddem, Lucas Gago +2
Purpose: To investigate whether Fractal Dimension (FD)-based oculomics could be used for individual risk prediction by evaluating repeatability and robustness. Methods: We used two…
Choroidalyzer: An open-source, end-to-end pipeline for choroidal analysis in optical coherence tomography
Justin Engelmann, Jamie Burke, Charlene Hamid +13
Purpose: To develop Choroidalyzer, an open-source, end-to-end pipeline for segmenting the choroid region, vessels, and fovea, and deriving choroidal thickness, area, and vascular i…
QuickQual: Lightweight, convenient retinal image quality scoring with off-the-shelf pretrained models
Justin Engelmann, Amos Storkey, Miguel O. Bernabeu
Image quality remains a key problem for both traditional and deep learning (DL)-based approaches to retinal image analysis, but identifying poor quality images can be time consumin…
An open-source deep learning algorithm for efficient and fully-automatic analysis of the choroid in optical coherence tomography
Jamie Burke, Justin Engelmann, Charlene Hamid +9
Purpose: To develop an open-source, fully-automatic deep learning algorithm, DeepGPET, for choroid region segmentation in optical coherence tomography (OCT) data. Methods: We used…
Detection of multiple retinal diseases in ultra-widefield fundus images using deep learning: data-driven identification of relevant regions
Justin Engelmann, Alice D. McTrusty, Ian J. C. MacCormick +3
Ultra-widefield (UWF) imaging is a promising modality that captures a larger retinal field of view compared to traditional fundus photography. Previous studies showed that deep lea…
Global explainability in aligned image modalities
Justin Engelmann, Amos Storkey, Miguel O. Bernabeu
Deep learning (DL) models are very effective on many computer vision problems and increasingly used in critical applications. They are also inherently black box. A number of method…
Conditional Wasserstein GAN-based Oversampling of Tabular Data for Imbalanced Learning
Justin Engelmann, Stefan Lessmann
Class imbalance is a common problem in supervised learning and impedes the predictive performance of classification models. Popular countermeasures include oversampling the minorit…