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Yang D, Hu Q, Zhao S, Hu X, Gao X, Dai F, Zheng Y, Yang Y, Cheng Y. An optofluidic system for the concentration gradient screening of oocyte protection drugs. Talanta 2024; 278:126472. [PMID: 38924991 DOI: 10.1016/j.talanta.2024.126472] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2024] [Revised: 06/17/2024] [Accepted: 06/23/2024] [Indexed: 06/28/2024]
Abstract
Oocytes protective drug screening is essential for the treatment of reproductive diseases. However, few studies construct the oocyte in vitro drug screening microfluidic systems because of their enormous size, scarcity, and sensitivity to the culture environment. Here, we present an optofluidic system for oocyte drug screening and state analysis. The system consists of two parts: an open-top drug screening microfluidic chip and an optical Fourier filter analysis part. The open-top microfluidic chip anchors single oocyte with hydrogel and allows nutrient and gas environment updating which is essential for oocyte culturing. The optical filter analysis part is used to accurately analyse the status of oocytes. Based on this system, we found that fluorene-9-bisphenol (BHPF) damaged the oocyte spindle in a dose-dependent manner, a high dose of melatonin (10-3 M) effectively reduces the percentage of abnormally arranged chromosomes of oocytes exposed to 40 μM BHPF. This optofluidic system shows great promise for the culture of oocytes and demonstrates the robust ability for convenient multi-concentration oocytes drug screening. This technology may benefit further biomedicine and reproductive toxicology applications in the lab on a chip community.
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Affiliation(s)
- Dongyong Yang
- Department of Obstetrics and Gynecology, Renmin Hospital of Wuhan University, Wuhan, 430060, China
| | - Qinghao Hu
- Department of Clinical Laboratory, Institute of Medicine and Physics, Renmin Hospital of Wuhan University, Wuhan, 430072, China; Key Laboratory of Artificial Micro/Nano Structure of Ministry of Education, School of Physics & Technology, Wuhan University, Wuhan, 430072, China; Shenzhen Research Institute, Wuhan University, Shenzhen, 518000, China
| | - Shukun Zhao
- Department of Clinical Laboratory, Institute of Medicine and Physics, Renmin Hospital of Wuhan University, Wuhan, 430072, China; Key Laboratory of Artificial Micro/Nano Structure of Ministry of Education, School of Physics & Technology, Wuhan University, Wuhan, 430072, China; Shenzhen Research Institute, Wuhan University, Shenzhen, 518000, China
| | - Xuejia Hu
- Department of Electronic Engineering, School of Electronic Science and Engineering, Xiamen University, Xiamen, 361005, China
| | - Xiaoqi Gao
- Department of Clinical Laboratory, Institute of Medicine and Physics, Renmin Hospital of Wuhan University, Wuhan, 430072, China; Key Laboratory of Artificial Micro/Nano Structure of Ministry of Education, School of Physics & Technology, Wuhan University, Wuhan, 430072, China; Shenzhen Research Institute, Wuhan University, Shenzhen, 518000, China
| | - Fangfang Dai
- Department of Obstetrics and Gynecology, Renmin Hospital of Wuhan University, Wuhan, 430060, China
| | - Yajing Zheng
- Department of Obstetrics and Gynecology, Renmin Hospital of Wuhan University, Wuhan, 430060, China
| | - Yi Yang
- Department of Clinical Laboratory, Institute of Medicine and Physics, Renmin Hospital of Wuhan University, Wuhan, 430072, China; Key Laboratory of Artificial Micro/Nano Structure of Ministry of Education, School of Physics & Technology, Wuhan University, Wuhan, 430072, China; Shenzhen Research Institute, Wuhan University, Shenzhen, 518000, China.
| | - Yanxiang Cheng
- Department of Obstetrics and Gynecology, Renmin Hospital of Wuhan University, Wuhan, 430060, China.
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Wei J, Yu S, Du Y, Liu K, Xu Y, Xu X. Automatic Segmentation of Hyperreflective Foci in OCT Images Based on Lightweight DBR Network. J Digit Imaging 2023; 36:1148-1157. [PMID: 36749455 PMCID: PMC10287852 DOI: 10.1007/s10278-023-00786-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 01/14/2023] [Accepted: 01/18/2023] [Indexed: 02/08/2023] Open
Abstract
Hyperreflective foci (HF) reflects inflammatory responses for fundus diseases such as diabetic macular edema (DME), retina vein occlusion (RVO), and central serous chorioretinopathy (CSC). Shown as high contrast and reflectivity in optical coherence tomography (OCT) images, automatic segmentation of HF in OCT images is helpful for the prognosis of fundus diseases. Previous traditional methods were time-consuming and required high computing power. Hence, we proposed a lightweight network to segment HF (with a speed of 57 ms per OCT image, at least 150 ms faster than other methods). Our framework consists of two stages: an NLM filter and patch-based split to preprocess images and a lightweight DBR neural network to segment HF automatically. Experimental results from 3000 OCT images of 300 patients (100 DME,100 RVO, and 100 CSC) revealed that our method achieved HF segmentation successfully. The DBR network had the area under curves dice similarity coefficient (DSC) of 83.65%, 76.43%, and 82.20% in segmenting HF in DME, RVO, and CSC on the test cohort respectively. Our DBR network achieves at least 5% higher DSC than previous methods. HF in DME was more easily segmented compared with the other two types. In addition, our DBR network is universally applicable to clinical practice with the ability to segment HF in a wide range of fundus diseases.
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Affiliation(s)
- Jin Wei
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, National Clinical Research Center for Eye Diseases, Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai Engineering Center for Visual Science and Photomedicine, Shanghai Engineering Center for Precise Diagnosis and Treatment of Eye Diseases, Shanghai, 200080, China
- Shanghai Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China
| | - Suqin Yu
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, National Clinical Research Center for Eye Diseases, Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai Engineering Center for Visual Science and Photomedicine, Shanghai Engineering Center for Precise Diagnosis and Treatment of Eye Diseases, Shanghai, 200080, China
| | - Yuchen Du
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, National Clinical Research Center for Eye Diseases, Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai Engineering Center for Visual Science and Photomedicine, Shanghai Engineering Center for Precise Diagnosis and Treatment of Eye Diseases, Shanghai, 200080, China
- Department of Automation, Shanghai Jiao Tong University, Shanghai, 200240, China
| | - Kun Liu
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, National Clinical Research Center for Eye Diseases, Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai Engineering Center for Visual Science and Photomedicine, Shanghai Engineering Center for Precise Diagnosis and Treatment of Eye Diseases, Shanghai, 200080, China
| | - Yupeng Xu
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, National Clinical Research Center for Eye Diseases, Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai Engineering Center for Visual Science and Photomedicine, Shanghai Engineering Center for Precise Diagnosis and Treatment of Eye Diseases, Shanghai, 200080, China.
| | - Xun Xu
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, National Clinical Research Center for Eye Diseases, Shanghai Key Laboratory of Ocular Fundus Diseases, Shanghai Engineering Center for Visual Science and Photomedicine, Shanghai Engineering Center for Precise Diagnosis and Treatment of Eye Diseases, Shanghai, 200080, China
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Zhou Q, Wen M, Yu B, Lou C, Ding M, Zhang X. Self-supervised transformer based non-local means despeckling of optical coherence tomography images. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
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4
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Bian H, Wang J, Hong C, Liu L, Ji R, Cao S, Abdalla AN, Chen X. GPU-accelerated image registration algorithm in ophthalmic optical coherence tomography. BIOMEDICAL OPTICS EXPRESS 2023; 14:194-207. [PMID: 36698653 PMCID: PMC9841998 DOI: 10.1364/boe.479343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 11/28/2022] [Accepted: 11/28/2022] [Indexed: 06/17/2023]
Abstract
Limited to the power of the light source in ophthalmic optical coherence tomography (OCT), the signal-to-noise ratio (SNR) of the reconstructed images is usually lower than OCT used in other fields. As a result, improvement of the SNR is required. The traditional method is averaging several images at the same lateral position. However, the image registration average costs too much time, which limits its real-time imaging application. In response to this problem, graphics processing unit (GPU)-side kernel functions are applied to accelerate the reconstruction of the OCT signals in this paper. The SNR of the images reconstructed from different numbers of A-scans and B-scans were compared. The results demonstrated that: 1) There is no need to realize the axial registration with every A-scan. The number of the A-scans used to realize axial registration is suitable to set as ∼25, when the A-line speed was set as ∼12.5kHz. 2) On the basis of ensuring the quality of the reconstructed images, the GPU can achieve 43× speedup compared with CPU.
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Affiliation(s)
- Haiyi Bian
- Faculty of Electronic Information Engineering, Huaiyin Institute of Technology, Huai’an, Jiangsu, 223003, China
| | - Jingtao Wang
- School of Electronic and Information Engineering, Soochow University, 215006, Suzhou, China
| | - Chengjian Hong
- School of Electronic and Information Engineering, Soochow University, 215006, Suzhou, China
| | - Lei Liu
- Faculty of Electronic Information Engineering, Huaiyin Institute of Technology, Huai’an, Jiangsu, 223003, China
| | - Rendong Ji
- Faculty of Electronic Information Engineering, Huaiyin Institute of Technology, Huai’an, Jiangsu, 223003, China
| | - Suqun Cao
- Faculty of Electronic Information Engineering, Huaiyin Institute of Technology, Huai’an, Jiangsu, 223003, China
| | - Ahmed N. Abdalla
- Faculty of Electronic Information Engineering, Huaiyin Institute of Technology, Huai’an, Jiangsu, 223003, China
| | - Xinjian Chen
- Faculty of Electronic Information Engineering, Huaiyin Institute of Technology, Huai’an, Jiangsu, 223003, China
- School of Electronic and Information Engineering, Soochow University, 215006, Suzhou, China
- State Key Laboratory of Radiation Medicine and Protection, Soochow University, 215123, Suzhou, China
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Chen H, Gao J. Non-Local Mean Denoising Algorithm Based on Fractional Compact Finite Difference Scheme Effectively Reduces Speckle Noise in Optical Coherence Tomography Images. MICROMACHINES 2022; 13:2039. [PMID: 36557339 PMCID: PMC9781262 DOI: 10.3390/mi13122039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 11/18/2022] [Accepted: 11/19/2022] [Indexed: 06/17/2023]
Abstract
Optical coherence tomography (OCT) is used in various fields such, as medical diagnosis and material inspection, as a non-invasive and high-resolution optical imaging modality. However, an OCT image is damaged by speckle noise during its generation, thus reducing the image quality. To address this problem, a non-local means (NLM) algorithm based on the fractional compact finite difference scheme (FCFDS) is proposed to remove the speckle noise in OCT images. FCFDS uses more local pixel information when compared to integer-order difference operators. The FCFDS operator is introduced into the NLM algorithm to construct a high-precision weight calculation so that the proposed algorithm can effectively reduce the speckle noise in the OCT images. Experiments on simulations and real OCT images show that the proposed method is comparable to other state-of-the-art despeckling methods and can substantially reduce noise and preserve image details such as edges and structures. Speckle noise removal can further promote the application of the proposed algorithm in medical diagnosis and industrial detection, as it has key research value.
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Affiliation(s)
- Huaiguang Chen
- School of Science, Shandong Jianzhu University, Jinan 250101, China
- Center for Engineering Computation and Software Development, Shandong Jianzhu University, Jinan 250101, China
| | - Jing Gao
- School of Science, Shandong Jianzhu University, Jinan 250101, China
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Varadarajan D, Magnain C, Fogarty M, Boas DA, Fischl B, Wang H. A novel algorithm for multiplicative speckle noise reduction in ex vivo human brain OCT images. Neuroimage 2022; 257:119304. [PMID: 35568350 PMCID: PMC10018743 DOI: 10.1016/j.neuroimage.2022.119304] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Revised: 05/06/2022] [Accepted: 05/10/2022] [Indexed: 10/18/2022] Open
Abstract
Optical coherence tomography (OCT) images of ex vivo human brain tissue are corrupted by multiplicative speckle noise that degrades the contrast to noise ratio (CNR) of microstructural compartments. This work proposes a novel algorithm to reduce noise corruption in OCT images that minimizes the penalized negative log likelihood of gamma distributed speckle noise. The proposed method is formulated as a majorize-minimize problem that reduces to solving an iterative regularized least squares optimization. We demonstrate the usefulness of the proposed method by removing speckle in simulated data, phantom data and real OCT images of human brain tissue. We compare the proposed method with state of the art filtering and non-local means based denoising methods. We demonstrate that our approach removes speckle accurately, improves CNR between different tissue types and better preserves small features and edges in human brain tissue.
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Affiliation(s)
- Divya Varadarajan
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA; Radiology, Harvard Medical School, Boston, MA 02115, USA.
| | - Caroline Magnain
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA; Radiology, Harvard Medical School, Boston, MA 02115, USA
| | - Morgan Fogarty
- Imaging Science Program, Washington University McKelvey School of Engineering, St. Louis, MO 63130, USA; Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - David A Boas
- Biomedical Engineering and Electrical and Computer Engineering, Boston University, Boston, MA 02215, USA
| | - Bruce Fischl
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA; Radiology, Harvard Medical School, Boston, MA 02115, USA; Harvard-MIT Health Science and Technology, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Hui Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA; Radiology, Harvard Medical School, Boston, MA 02115, USA
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Liu L, Zhai Z, Zhang T, Fan L. Reducing speckle in anterior segment optical coherence tomography images based on a convolutional neural network. APPLIED OPTICS 2021; 60:10964-10974. [PMID: 35200859 DOI: 10.1364/ao.442678] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Accepted: 11/15/2021] [Indexed: 06/14/2023]
Abstract
Speckle noise is ubiquitous in the optical coherence tomography (OCT) image of the anterior segment, which greatly affects the image quality and destroys the relevant structural information. In order to reduce the influence of speckle noise in OCT images, a denoising algorithm based on a convolutional neural network is proposed in this paper. Unlike traditional algorithms that directly obtain denoised images, the algorithm model proposed in this paper learns the speckle noise distribution through the constructed trainable OCT dataset and indirectly obtains the denoised result image. In order to verify the performance of the model, we compare the denoising results of the algorithm proposed in this paper with several state-of-the-art algorithms from three perspectives: qualitative evaluation from the subjective visual perspective, quantitative evaluation from objective parameter indicators, and running time. The experimental results show that the proposed algorithm has a good denoising effect on different OCT images of the anterior segment and has good generalization ability. Besides, it retains the relevant details and texture information in the image, and it has strong edge preserving ability. The image of OCT speckle removal can be obtained within 0.4 s, which meets the time limit requirement of clinical application.
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Shen Z, Xi M, Tang C, Xu M, Lei Z. Double-path parallel convolutional neural network for removing speckle noise in different types of OCT images. APPLIED OPTICS 2021; 60:4345-4355. [PMID: 34143124 DOI: 10.1364/ao.419871] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Accepted: 04/30/2021] [Indexed: 06/12/2023]
Abstract
Speckle noises widely exist in optical coherence tomography (OCT) images. We propose an improved double-path parallel convolutional neural network (called DPNet) to reduce speckles. We increase the network width to replace the network depth to extract deeper information from the original OCT images. In addition, we use dilated convolution and residual learning to increase the learning ability of our DPNet. We use 100 pairs of human retinal OCT images as the training dataset. Then we test the DPNet model for denoising speckles on four different types of OCT images, mainly including human retinal OCT images, skin OCT images, colon crypt OCT images, and quail embryo OCT images. We compare the DPNet model with the adaptive complex diffusion method, the curvelet shrinkage method, the shearlet-based total variation method, and the OCTNet method. We qualitatively and quantitatively evaluate these methods in terms of image smoothness, structural information protection, and edge clarity. Our experimental results prove the performance of the DPNet model, and it allows us to batch and quickly process different types of poor-quality OCT images without any parameter fine-tuning under a time-constrained situation.
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