Spectral Bandwidth Recovery of Optical Coherence Tomography Images using Deep Learning

no code implementations • 2 Jan 2023 • Timothy T. Yu, Da Ma, Jayden Cole, Myeong Jin Ju, Mirza F. Beg, Marinko V. Sarunic

Optical coherence tomography (OCT) captures cross-sectional data and is used for the screening, monitoring, and treatment planning of retinal diseases.

Decision Making Generative Adversarial Network +1

Segmentation-guided Domain Adaptation and Data Harmonization of Multi-device Retinal Optical Coherence Tomography using Cycle-Consistent Generative Adversarial Networks

no code implementations • 31 Aug 2022 • Shuo Chen, Da Ma, Sieun Lee, Timothy T. L. Yu, Gavin Xu, Donghuan Lu, Karteek Popuri, Myeong Jin Ju, Marinko V. Sarunic, Mirza Faisal Beg

Optical Coherence Tomography(OCT) is a non-invasive technique capturing cross-sectional area of the retina in micro-meter resolutions.

Domain Adaptation Hallucination +1

Domain Adaptation via CycleGAN for Retina Segmentation in Optical Coherence Tomography

no code implementations • 6 Jul 2021 • Ricky Chen, Timothy T. Yu, Gavin Xu, Da Ma, Marinko V. Sarunic, Mirza Faisal Beg

In this study, we investigated a learning-based approach of adapting the domain of a publicly available dataset, UK Biobank dataset (UKB).

Decision Making Domain Adaptation

Microvasculature Segmentation and Inter-capillary Area Quantification of the Deep Vascular Complex using Transfer Learning

no code implementations • 19 Mar 2020 • Julian Lo, Morgan Heisler, Vinicius Vanzan, Sonja Karst, Ivana Zadro Matovinovic, Sven Loncaric, Eduardo V. Navajas, Mirza Faisal Beg, Marinko V. Sarunic

The inter-rater comparisons for the SCP had an accuracy and Dice index of 0. 8300 and 0. 6700, respectively, and 0. 6874 and 0. 7416 for the DVC.

Transfer Learning

Cascade Dual-branch Deep Neural Networks for Retinal Layer and fluid Segmentation of Optical Coherence Tomography Incorporating Spatial Prior

no code implementations • MIDL 2019 • Da Ma, Donghuan Lu, Morgan Heisler, Setareh Dabiri, Sieun Lee, Gavin Weiguan Ding, Marinko V. Sarunic, Mirza Faisal Beg

Optical coherence tomography (OCT) is a non-invasive imaging technology that can provide micrometer-resolution cross-sectional images of the inner structures of the eye.

Cascaded Deep Neural Networks for Retinal Layer Segmentation of Optical Coherence Tomography with Fluid Presence

no code implementations • 7 Dec 2019 • Donghuan Lu, Morgan Heisler, Da Ma, Setareh Dabiri, Sieun Lee, Gavin Weiguang Ding, Marinko V. Sarunic, Mirza Faisal Beg

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.

Deep learning vessel segmentation and quantification of the foveal avascular zone using commercial and prototype OCT-A platforms

no code implementations • 25 Sep 2019 • Morgan Heisler, Forson Chan, Zaid Mammo, Chandrakumar Balaratnasingam, Pavle Prentasic, Gavin Docherty, MyeongJin Ju, Sanjeeva Rajapakse, Sieun Lee, Andrew Merkur, Andrew Kirker, David Albiani, David Maberley, K. Bailey Freund, Mirza Faisal Beg, Sven Loncaric, Marinko V. Sarunic, Eduardo V. Navajas

This study demonstrates the utility of deep neural networks for automatic quantification of foveal avascular zone (FAZ) parameters and perifoveal vessel density of OCT-A images in healthy and diabetic eyes.

Specificity

Retinal Fluid Segmentation and Detection in Optical Coherence Tomography Images using Fully Convolutional Neural Network

no code implementations • 13 Oct 2017 • Donghuan Lu, Morgan Heisler, Sieun Lee, Gavin Ding, Marinko V. Sarunic, Mirza Faisal Beg

As a non-invasive imaging modality, optical coherence tomography (OCT) can provide micrometer-resolution 3D images of retinal structures.

General Classification Segmentation