Toward Ground-Truth Optical Coherence Tomography via Three-Dimensional Unsupervised Deep Learning Processing and Data.
IEEE Trans Med Imaging
; 43(6): 2395-2407, 2024 Jun.
Article
em En
| MEDLINE
| ID: mdl-38324426
ABSTRACT
Optical coherence tomography (OCT) can perform non-invasive high-resolution three-dimensional (3D) imaging and has been widely used in biomedical fields, while it is inevitably affected by coherence speckle noise which degrades OCT imaging performance and restricts its applications. Here we present a novel speckle-free OCT imaging strategy, named toward-ground-truth OCT ( t GT-OCT), that utilizes unsupervised 3D deep-learning processing and leverages OCT 3D imaging features to achieve speckle-free OCT imaging. Specifically, our proposed t GT-OCT utilizes an unsupervised 3D-convolution deep-learning network trained using random 3D volumetric data to distinguish and separate speckle from real structures in 3D imaging volumetric space; moreover, t GT-OCT effectively further reduces speckle noise and reveals structures that would otherwise be obscured by speckle noise while preserving spatial resolution. Results derived from different samples demonstrated the high-quality speckle-free 3D imaging performance of t GT-OCT and its advancement beyond the previous state-of-the-art. The code is available online https//github.com/Voluntino/tGT-OCT.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Imageamento Tridimensional
/
Tomografia de Coerência Óptica
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Aprendizado de Máquina não Supervisionado
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Aprendizado Profundo
Limite:
Animals
/
Humans
Idioma:
En
Revista:
IEEE Trans Med Imaging
Ano de publicação:
2024
Tipo de documento:
Article