Toward ground-truth optical coherence tomography via three-dimensional unsupervised deep learning processing and data
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866912452065624064 |
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| author | Wu, Renxiong Zheng, Fei Li, Meixuan Huang, Shaoyan Ge, Xin Liu, Linbo Liu, Yong Ni, Guangming |
| author_facet | Wu, Renxiong Zheng, Fei Li, Meixuan Huang, Shaoyan Ge, Xin Liu, Linbo Liu, Yong Ni, Guangming |
| contents | 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 (tGT-OCT), that utilizes unsupervised 3D deep-learning processing and leverages OCT 3D imaging features to achieve speckle-free OCT imaging. Specifically, our proposed tGT-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, tGT-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 tGT-OCT and its advancement beyond the previous state-of-the-art. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_03887 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Toward ground-truth optical coherence tomography via three-dimensional unsupervised deep learning processing and data Wu, Renxiong Zheng, Fei Li, Meixuan Huang, Shaoyan Ge, Xin Liu, Linbo Liu, Yong Ni, Guangming Optics Image and Video Processing Medical Physics 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 (tGT-OCT), that utilizes unsupervised 3D deep-learning processing and leverages OCT 3D imaging features to achieve speckle-free OCT imaging. Specifically, our proposed tGT-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, tGT-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 tGT-OCT and its advancement beyond the previous state-of-the-art. |
| title | Toward ground-truth optical coherence tomography via three-dimensional unsupervised deep learning processing and data |
| topic | Optics Image and Video Processing Medical Physics |
| url | https://arxiv.org/abs/2311.03887 |