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Article ; Online: High-precision Drosophila heart segmentation and dynamic cardiac parameter measurement for optogenetics-OCT-based cardiac function research.

Zheng, Fei / Wu, Renxiong / Huang, Shaoyan / Li, Meixuan / Yuan, Wuzhou / Ni, Guangming / Liu, Yong

2024 Volume 17, Issue 4, Page(s) e202300447

Abstract: Drosophila model has been widely used to study cardiac functions, especially combined with optogenetics and optical coherence tomography (OCT) that can continuously acquire mass cross-sectional images of the Drosophila heart in vivo over time. It's ... ...

Abstract	Drosophila model has been widely used to study cardiac functions, especially combined with optogenetics and optical coherence tomography (OCT) that can continuously acquire mass cross-sectional images of the Drosophila heart in vivo over time. It's urgent to quickly and accurately obtain dynamic Drosophila cardiac parameters such as heartbeat rate for cardiac function quantitative analysis through these mass cross-sectional images of the Drosophila heart. Here we present a deep-learning method that integrates U-Net and generative adversarial network architectures while incorporating residually connected convolutions for high-precision OCT image segmentation of Drosophila heart and dynamic cardiac parameter measurements for optogenetics-OCT-based cardiac function research. We compared our proposed network with the previous approaches and our segmentation results achieved the accuracy of intersection over union and Dice similarity coefficient higher than 98%, which can be used to better quantify dynamic heart parameters and improve the efficiency of Drosophila-model-based cardiac research via the optogenetics-OCT-based platform.
MeSH term(s)	Animals ; Drosophila ; Optogenetics ; Tomography, Optical Coherence ; Heart/diagnostic imaging ; Heart Rate ; Image Processing, Computer-Assisted
Language	English
Publishing date	2024-01-18
Publishing country	Germany
Document type	Journal Article
ZDB-ID	2390063-5
ISSN	1864-0648 ; 1864-063X
ISSN (online)	1864-0648
ISSN	1864-063X
DOI	10.1002/jbio.202300447
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Zs.A 6723: Show issues

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Article ; Online: Depth-resolved transverse-plane motion tracking with configurable measurement features via optical coherence tomography.

Ni, Guangming / Wu, Renxiong / Zhong, Junming / Liu, Yong

Optics express

2022 Volume 30, Issue 8, Page(s) 12215–12227

Abstract: Optical coherence tomography (OCT), a promising noninvasive bioimaging technique, has become one of the most successful optical technologies implemented in medicine and clinical practice. Here we report a novel technique of depth-resolved transverse- ... ...

Abstract	Optical coherence tomography (OCT), a promising noninvasive bioimaging technique, has become one of the most successful optical technologies implemented in medicine and clinical practice. Here we report a novel technique of depth-resolved transverse-plane motion tracking with configurable measurement features via optical coherence tomography, termed OCT-MT. Based on OCT circular scanning combined with speckle spatial oversampling, the OCT-MT technique can perform depth-resolved transverse-plane motion tracking. Benefitting from the optical interference and depth-resolved feature, the proposed OCT-MT can reduce the requirements on the input power of the irradiation signal and the surface reflectivity and roughness of the target, when performing motion tracking. Furthermore, OCT-MT can conduct such kind of motion tracking with configurable measurement ranges and resolutions by configuring A-line number per scanning circle, circular scanning radius, and A-line scanning time. The proposed OCT-MT technique may expand the ability of motion tracking for OCT in addition to imaging.
MeSH term(s)	Tomography, Optical Coherence/methods
Language	English
Publishing date	2022-04-19
Publishing country	United States
Document type	Journal Article
ZDB-ID	1491859-6
ISSN	1094-4087 ; 1094-4087
ISSN (online)	1094-4087
ISSN	1094-4087
DOI	10.1364/OE.450590
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: SNR-Net OCT: brighten and denoise low-light optical coherence tomography images via deep learning.

Huang, Shaoyan / Wang, Rong / Wu, Renxiong / Zhong, Junming / Ge, Xin / Liu, Yong / Ni, Guangming

Optics express

2023 Volume 31, Issue 13, Page(s) 20696–20714

Abstract: Low-light optical coherence tomography (OCT) images generated when using low input power, low-quantum-efficiency detection units, low exposure time, or facing high-reflective surfaces, have low bright and signal-to-noise rates (SNR), and restrict OCT ... ...

Abstract	Low-light optical coherence tomography (OCT) images generated when using low input power, low-quantum-efficiency detection units, low exposure time, or facing high-reflective surfaces, have low bright and signal-to-noise rates (SNR), and restrict OCT technique and clinical applications. While low input power, low quantum efficiency, and low exposure time can help reduce the hardware requirements and accelerate imaging speed; high-reflective surfaces are unavoidable sometimes. Here we propose a deep-learning-based technique to brighten and denoise low-light OCT images, termed SNR-Net OCT. The proposed SNR-Net OCT deeply integrated a conventional OCT setup and a residual-dense-block U-Net generative adversarial network with channel-wise attention connections trained using a customized large speckle-free SNR-enhanced brighter OCT dataset. Results demonstrated that the proposed SNR-Net OCT can brighten low-light OCT images and remove the speckle noise effectively, with enhancing SNR and maintaining the tissue microstructures well. Moreover, compared to the hardware-based techniques, the proposed SNR-Net OCT can be of lower cost and better performance.
Language	English
Publishing date	2023-06-28
Publishing country	United States
Document type	Journal Article
ZDB-ID	1491859-6
ISSN	1094-4087 ; 1094-4087
ISSN (online)	1094-4087
ISSN	1094-4087
DOI	10.1364/OE.491391
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: 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

IEEE transactions on medical imaging

2024 Volume PP

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 ... ...

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 (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. The code is available online: https://github.com/Voluntino/tGT-OCT.
Language	English
Publishing date	2024-02-07
Publishing country	United States
Document type	Journal Article
ZDB-ID	622531-7
ISSN	1558-254X ; 0278-0062
ISSN (online)	1558-254X
ISSN	0278-0062
DOI	10.1109/TMI.2024.3363416
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Zs.A 2546: Show issues

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Jg. 1995 - 2021: Lesesall (2.OG)
ab Jg. 2022: Lesesaal (EG)

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Article ; Online: Unsupervised OCT image despeckling with ground-truth- and repeated-scanning-free features.

Wu, Renxiong / Huang, Shaoyan / Zhong, Junming / Zheng, Fei / Li, Meixuan / Ge, Xin / Zhong, Jie / Liu, Linbo / Ni, Guangming / Liu, Yong

Optics express

2024 Volume 32, Issue 7, Page(s) 11934–11951

Abstract: Optical coherence tomography (OCT) can resolve biological three-dimensional tissue structures, but it is inevitably plagued by speckle noise that degrades image quality and obscures biological structure. Recently unsupervised deep learning methods are ... ...

Abstract	Optical coherence tomography (OCT) can resolve biological three-dimensional tissue structures, but it is inevitably plagued by speckle noise that degrades image quality and obscures biological structure. Recently unsupervised deep learning methods are becoming more popular in OCT despeckling but they still have to use unpaired noisy-clean images or paired noisy-noisy images. To address the above problem, we propose what we believe to be a novel unsupervised deep learning method for OCT despeckling, termed Double-free Net, which eliminates the need for ground truth data and repeated scanning by sub-sampling noisy images and synthesizing noisier images. In comparison to existing unsupervised methods, Double-free Net obtains superior denoising performance when trained on datasets comprising retinal and human tissue images without clean images. The efficacy of Double-free Net in denoising holds significant promise for diagnostic applications in retinal pathologies and enhances the accuracy of retinal layer segmentation. Results demonstrate that Double-free Net outperforms state-of-the-art methods and exhibits strong convenience and adaptability across different OCT images.
MeSH term(s)	Humans ; Tomography, Optical Coherence/methods ; Algorithms ; Retina/diagnostic imaging ; Radionuclide Imaging ; Image Processing, Computer-Assisted/methods
Language	English
Publishing date	2024-04-04
Publishing country	United States
Document type	Journal Article
ZDB-ID	1491859-6
ISSN	1094-4087 ; 1094-4087
ISSN (online)	1094-4087
ISSN	1094-4087
DOI	10.1364/OE.510696
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: MAS-Net OCT: a deep-learning-based speckle-free multiple aperture synthetic optical coherence tomography.

Wu, Renxiong / Huang, Shaoyan / Zhong, Junming / Li, Meixuan / Zheng, Fei / Bo, En / Liu, Linbo / Liu, Yong / Ge, Xin / Ni, Guangming

Biomedical optics express

2023 Volume 14, Issue 6, Page(s) 2591–2607

Abstract: High-resolution spectral domain optical coherence tomography (SD-OCT) is a vital clinical technique that suffers from the inherent compromise between transverse resolution and depth of focus (DOF). Meanwhile, speckle noise worsens OCT imaging resolving ... ...

Abstract	High-resolution spectral domain optical coherence tomography (SD-OCT) is a vital clinical technique that suffers from the inherent compromise between transverse resolution and depth of focus (DOF). Meanwhile, speckle noise worsens OCT imaging resolving power and restricts potential resolution-enhancement techniques. Multiple aperture synthetic (MAS) OCT transmits light signals and records sample echoes along a synthetic aperture to extend DOF, acquired by time-encoding or optical path length encoding. In this work, a deep-learning-based multiple aperture synthetic OCT termed MAS-Net OCT, which integrated a speckle-free model based on self-supervised learning, was proposed. MAS-Net was trained on datasets generated by the MAS OCT system. Here we performed experiments on homemade microparticle samples and various biological tissues. Results demonstrated that the proposed MAS-Net OCT could effectively improve the transverse resolution in a large imaging depth as well as reduced most speckle noise.
Language	English
Publishing date	2023-05-10
Publishing country	United States
Document type	Journal Article
ZDB-ID	2572216-5
ISSN	2156-7085
ISSN	2156-7085
DOI	10.1364/BOE.483740
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: High-accuracy 3D segmentation of wet age-related macular degeneration via multi-scale and cross-channel feature extraction and channel attention.

Li, Meixuan / Shen, Yadan / Wu, Renxiong / Huang, Shaoyan / Zheng, Fei / Chen, Sizhu / Wang, Rong / Dong, Wentao / Zhong, Jie / Ni, Guangming / Liu, Yong

Biomedical optics express

2024 Volume 15, Issue 2, Page(s) 1115–1131

Abstract: Wet age-related macular degeneration (AMD) is the leading cause of visual impairment and vision loss in the elderly, and optical coherence tomography (OCT) enables revolving biotissue three-dimensional micro-structure widely used to diagnose and monitor ... ...

Abstract	Wet age-related macular degeneration (AMD) is the leading cause of visual impairment and vision loss in the elderly, and optical coherence tomography (OCT) enables revolving biotissue three-dimensional micro-structure widely used to diagnose and monitor wet AMD lesions. Many wet AMD segmentation methods based on deep learning have achieved good results, but these segmentation results are two-dimensional, and cannot take full advantage of OCT's three-dimensional (3D) imaging characteristics. Here we propose a novel deep-learning network characterizing multi-scale and cross-channel feature extraction and channel attention to obtain high-accuracy 3D segmentation results of wet AMD lesions and show the 3D specific morphology, a task unattainable with traditional two-dimensional segmentation. This probably helps to understand the ophthalmologic disease and provides great convenience for the clinical diagnosis and treatment of wet AMD.
Language	English
Publishing date	2024-01-26
Publishing country	United States
Document type	Journal Article
ZDB-ID	2572216-5
ISSN	2156-7085
ISSN	2156-7085
DOI	10.1364/BOE.513619
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Sm-Net OCT: a deep-learning-based speckle-modulating optical coherence tomography.

Ni, Guangming / Chen, Ying / Wu, Renxiong / Wang, Xiaoshan / Zeng, Ming / Liu, Yong

Optics express

2021 Volume 29, Issue 16, Page(s) 25511–25523

Abstract: Speckle imposes obvious limitations on resolving capabilities of optical coherence tomography (OCT), while speckle-modulating OCT can efficiently reduce speckle arbitrarily. However, speckle-modulating OCT seriously reduces the imaging sensitivity and ... ...

Abstract	Speckle imposes obvious limitations on resolving capabilities of optical coherence tomography (OCT), while speckle-modulating OCT can efficiently reduce speckle arbitrarily. However, speckle-modulating OCT seriously reduces the imaging sensitivity and temporal resolution of the OCT system when reducing speckle. Here, we proposed a deep-learning-based speckle-modulating OCT, termed Sm-Net OCT, by deeply integrating conventional OCT setup and generative adversarial network trained with a customized large speckle-modulating OCT dataset containing massive speckle patterns. The customized large speckle-modulating OCT dataset was obtained from the aforementioned conventional OCT setup rebuilt into a speckle-modulating OCT and performed imaging using different scanning parameters. Experimental results demonstrated that the proposed Sm-Net OCT can effectively obtain high-quality OCT images without the electronic noise and speckle, and conquer the limitations of reducing the imaging sensitivity and temporal resolution which conventional speckle-modulating OCT has. The proposed Sm-Net OCT can significantly improve the adaptability and practicality capabilities of OCT imaging, and expand its application fields.
Language	English
Publishing date	2021-09-13
Publishing country	United States
Document type	Journal Article
ZDB-ID	1491859-6
ISSN	1094-4087 ; 1094-4087
ISSN (online)	1094-4087
ISSN	1094-4087
DOI	10.1364/OE.431475
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Hybrid-structure network and network comparative study for deep-learning-based speckle-modulating optical coherence tomography.

Ni, Guangming / Wu, Renxiong / Zhong, Junming / Chen, Ying / Wan, Ling / Xie, Yao / Mei, Jie / Liu, Yong

Optics express

2022 Volume 30, Issue 11, Page(s) 18919–18938

Abstract: Optical coherence tomography (OCT), a promising noninvasive bioimaging technique, can resolve sample three-dimensional microstructures. However, speckle noise imposes obvious limitations on OCT resolving capabilities. Here we proposed a deep-learning- ... ...

Abstract	Optical coherence tomography (OCT), a promising noninvasive bioimaging technique, can resolve sample three-dimensional microstructures. However, speckle noise imposes obvious limitations on OCT resolving capabilities. Here we proposed a deep-learning-based speckle-modulating OCT based on a hybrid-structure network, residual-dense-block U-Net generative adversarial network (RDBU-Net GAN), and further conducted a comprehensively comparative study to explore multi-type deep-learning architectures' abilities to extract speckle pattern characteristics and remove speckle, and resolve microstructures. This is the first time that network comparative study has been performed on a customized dataset containing mass more-general speckle patterns obtained from a custom-built speckle-modulating OCT, but not on retinal OCT datasets with limited speckle patterns. Results demonstrated that the proposed RDBU-Net GAN has a more excellent ability to extract speckle pattern characteristics and remove speckle, and resolve microstructures. This work will be useful for future studies on OCT speckle removing and deep-learning-based speckle-modulating OCT.
MeSH term(s)	Deep Learning ; Retina/diagnostic imaging ; Tomography, Optical Coherence/methods
Language	English
Publishing date	2022-06-10
Publishing country	United States
Document type	Journal Article
ZDB-ID	1491859-6
ISSN	1094-4087 ; 1094-4087
ISSN (online)	1094-4087
ISSN	1094-4087
DOI	10.1364/OE.454504
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: SDoF-Net: Super Depth of Field Network for Cell Detection in Leucorrhea Micrograph.

Du, Xiaohui / Wang, Xiangzhou / Ni, Guangming / Zhang, Jing / Hao, Ruqian / Zhao, Jiaxi / Wang, Xudong / Liu, Juanxiu / Liu, Lin

IEEE journal of biomedical and health informatics

2022 Volume 26, Issue 3, Page(s) 1229–1238

Abstract: Accompanied with the rapid increase of the demand for routine examination of leucorrhea, efficiency and accuracy become the primary task. However, in super depth of field (SDoF) system, the problem of automatic detection and localization of cells in ... ...

Abstract	Accompanied with the rapid increase of the demand for routine examination of leucorrhea, efficiency and accuracy become the primary task. However, in super depth of field (SDoF) system, the problem of automatic detection and localization of cells in leucorrhea micro-images is still a big challenge. The changing of the relative position between the cell center and focus plane of microscope lead to variable cell morphological structure in the two-dimensional image, which is an important reason for the low accuracy of current deep learning target detection algorithms. In this paper, an object detection method based on Retinanet in state of super depth of field is proposed, which can achieve high precision detecting of leucorrhea components by the SDoF feature aggregation module. Compared with the current mainstream algorithms, the mean average accuracy (mAP) index has been improved significantly, the mAP index is 82.7% for SDoF module and 83.0% for SDoF+ module, with an average increase of more than 10%. These improved features can significantly improve the efficiency and accuracy of the algorithm. The algorithm proposed in this paper can be integrated into the leucorrhea automatic detection system.
MeSH term(s)	Algorithms ; Humans ; Microscopy
Language	English
Publishing date	2022-03-07
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	2695320-1
ISSN	2168-2208 ; 2168-2194
ISSN (online)	2168-2208
ISSN	2168-2194
DOI	10.1109/JBHI.2021.3101886
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Zs.A 5483: Show issues

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Jg. 1995 - 2021: Lesesall (2.OG)
ab Jg. 2022: Lesesaal (EG)

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