Automated inter-device 3D OCT image registration using deep learning and retinal layer segmentation
Use this link to cite
http://hdl.handle.net/2183/37792Collections
- GI-VARPA - Artigos [76]
Metadata
Show full item recordTitle
Automated inter-device 3D OCT image registration using deep learning and retinal layer segmentationAuthor(s)
Date
2023-07Citation
David Rivas-Villar, Alice R. Motschi, Michael Pircher, Christoph K. Hitzenberger, Markus Schranz, Philipp K. Roberts, Ursula Schmidt-Erfurth, and Hrvoje Bogunović, "Automated inter-device 3D OCT image registration using deep learning and retinal layer segmentation," Biomed. Opt. Express 14, 3726-3747 (2023). https://doi.org/10.1364/BOE.493047
Abstract
[Abstract]: Optical coherence tomography (OCT) is the most widely used imaging modality in ophthalmology. There are multiple variations of OCT imaging capable of producing complementary information. Thus, registering these complementary volumes is desirable in order to combine their information. In this work, we propose a novel automated pipeline to register OCT images produced by different devices. This pipeline is based on two steps: a multi-modal 2D en-face registration based on deep learning, and a Z-axis (axial axis) registration based on the retinal layer segmentation. We evaluate our method using data from a Heidelberg Spectralis and an experimental PS-OCT device. The empirical results demonstrated high-quality registrations, with mean errors of approximately 46 µm for the 2D registration and 9.59 µm for the Z-axis registration. These registrations may help in multiple clinical applications such as the validation of layer segmentations among others.
Keywords
Optical tomography
Deep learning
Image segmentation
Ophthalmology
Pipelines
Deep learning
Image segmentation
Ophthalmology
Pipelines
Editor version
Rights
Atribución 3.0 España
ISSN
2156-7085