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Wu W, Gong Y, Hao H, Zhang J, Su P, Yan Q, Ma Y, Zhao Y. Choroidal layer segmentation in OCT images by a boundary enhancement network. Front Cell Dev Biol 2022; 10:1060241. [PMID: 36438560 PMCID: PMC9691264 DOI: 10.3389/fcell.2022.1060241] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/03/2022] [Accepted: 10/25/2022] [Indexed: 12/23/2023] Open
Abstract
Morphological changes of the choroid have been proved to be associated with the occurrence and pathological mechanism of many ophthalmic diseases. Optical Coherence Tomography (OCT) is a non-invasive technique for imaging of ocular biological tissues, that can reveal the structure of the retinal and choroidal layers in micron-scale resolution. However, unlike the retinal layer, the interface between the choroidal layer and the sclera is ambiguous in OCT, which makes it difficult for ophthalmologists to identify with certainty. In this paper, we propose a novel boundary-enhanced encoder-decoder architecture for choroid segmentation in retinal OCT images, in which a Boundary Enhancement Module (BEM) forms the backbone of each encoder-decoder layer. The BEM consists of three parallel branches: 1) a Feature Extraction Branch (FEB) to obtain feature maps with different receptive fields; 2) a Channel Enhancement Branch (CEB) to extract the boundary information of different channels; and 3) a Boundary Activation Branch (BAB) to enhance the boundary information via a novel activation function. In addition, in order to incorporate expert knowledge into the segmentation network, soft key point maps are generated on the choroidal boundary, and are combined with the predicted images to facilitate precise choroidal boundary segmentation. In order to validate the effectiveness and superiority of the proposed method, both qualitative and quantitative evaluations are employed on three retinal OCT datasets for choroid segmentation. The experimental results demonstrate that the proposed method yields better choroid segmentation performance than other deep learning approaches. Moreover, both 2D and 3D features are extracted for statistical analysis from normal and highly myopic subjects based on the choroid segmentation results, which is helpful in revealing the pathology of high myopia. Code is available at https://github.com/iMED-Lab/Choroid-segmentation.
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Affiliation(s)
- Wenjun Wu
- Ningbo Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- University of Chinese Academy of Sciences, Beijing, China
| | - Yan Gong
- The Affiliated Ningbo Eye Hospital of Wenzhou Medical University, Ningbo, China
| | - Huaying Hao
- Ningbo Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
| | - Jiong Zhang
- Ningbo Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
| | - Pan Su
- School of Control and Computer Engineering North China Electric Power University, Baoding, China
| | - Qifeng Yan
- Ningbo Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
| | - Yuhui Ma
- Ningbo Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
| | - Yitian Zhao
- Ningbo Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
- Zhejiang Engineering Research Center for Biomedical Materials, Ningbo Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
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Wu N, Yi M, Guan C, Wang M, Zhang Z, Yang X, Li H, Han D, Zeng Y, Tang Z. Retinal cross-section motion correction in three-dimensional retinal optical coherence tomography. J Biophotonics 2021; 14:e202000443. [PMID: 33576160 DOI: 10.1002/jbio.202000443] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2020] [Revised: 12/15/2020] [Accepted: 02/08/2021] [Indexed: 06/12/2023]
Abstract
Motion correction is an important issue in ophthalmic optical coherence tomography (OCT), and can improve the ability of data sets to reflect the physiological structures of tissues and make visualization and subsequent analysis easier. In this study, we present a novel method to correct the cross-sectional motion artifacts in retinal OCT volumes. Motion along the x-direction (fast-scan direction) is corrected through the normalized cross-correlation algorithm, while axial motion compensation is performed using the polynomial fitting method on the inner segment/outer segment (IS/OS) layer segmented by the shortest path faster algorithm (SPFA). The results of volunteers with central serous chorioretinopathy demonstrate that the proposed method effectively corrects motion artifacts in OCT volumes and may have potential application value in the evaluation of ophthalmic diseases such as diabetic retinopathy, glaucoma and age-related macular degeneration.
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Affiliation(s)
- Nanshou Wu
- School of Physics and Telecommunication Engineering, South China Normal University, Guangzhou, China
| | - Min Yi
- School of Physics and Optoelectronic Engineering, Foshan University, Foshan, China
| | - Caizhong Guan
- School of Physics and Optoelectronic Engineering, Foshan University, Foshan, China
| | - Mingyi Wang
- School of Physics and Optoelectronic Engineering, Foshan University, Foshan, China
| | - Zhang Zhang
- School of Mechanical and Electrical Engineering and Automation, Foshan University, Foshan, China
| | - Xulun Yang
- School of Mechanical and Electrical Engineering and Automation, Foshan University, Foshan, China
| | - Hongyi Li
- School of Physics and Optoelectronic Engineering, Foshan University, Foshan, China
| | - Dingan Han
- School of Physics and Optoelectronic Engineering, Foshan University, Foshan, China
| | - Yaguang Zeng
- School of Physics and Optoelectronic Engineering, Foshan University, Foshan, China
| | - Zhilie Tang
- School of Physics and Telecommunication Engineering, South China Normal University, Guangzhou, China
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Li L, Jia T. Optical Coherence Tomography Vulnerable Plaque Segmentation Based on Deep Residual U-Net. Rev Cardiovasc Med 2020; 20:171-177. [PMID: 31601091 DOI: 10.31083/j.rcm.2019.03.5201] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2019] [Accepted: 08/14/2019] [Indexed: 11/06/2022] Open
Abstract
Automatic and accurate segmentation of intravascular optical coherence tomography imagery is of great importance in computer-aided diagnosis and in treatment of cardiovascular diseases. However, this task has not been well addressed for two reasons. First, because of the difficulty of acquisition, and the laborious labeling from personnel, optical coherence tomography image datasets are usually small. Second, optical coherence tomography images contain a variety of imaging artifacts, which hinder a clear observation of the vascular wall. In order to overcome these limitations, a new method of cardiovascular vulnerable plaque segmentation is proposed. This method constructs a novel Deep Residual U-Net to segment vulnerable plaque regions. Furthermore, in order to overcome the inaccuracy in object boundary segmentation which previous research has shown extensively, a loss function consisting of weighted cross-entropy loss and Dice coefficient is proposed to solve this problem. Thorough experiments and analysis have been carried out to verify the effectiveness and superior performance of the proposed method.
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Affiliation(s)
- Lincan Li
- College of Mechanical Engineering and Automation, Northeastern University, Shenyang, 110004, P. R. China
| | - Tong Jia
- College of Information Science and Engineering, Northeastern University, Shenyang, 110004, P. R. China
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Le NT, Le DH, Wang JW, Wang CC. Entropy-Based Clustering Algorithm for Fingerprint Singular Point Detection. Entropy (Basel) 2019; 21:e21080786. [PMID: 33267499 PMCID: PMC7515315 DOI: 10.3390/e21080786] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/19/2019] [Revised: 08/06/2019] [Accepted: 08/09/2019] [Indexed: 11/16/2022]
Abstract
Fingerprints have long been used in automated fingerprint identification or verification systems. Singular points (SPs), namely the core and delta point, are the basic features widely used for fingerprint registration, orientation field estimation, and fingerprint classification. In this study, we propose an adaptive method to detect SPs in a fingerprint image. The algorithm consists of three stages. First, an innovative enhancement method based on singular value decomposition is applied to remove the background of the fingerprint image. Second, a blurring detection and boundary segmentation algorithm based on the innovative image enhancement is proposed to detect the region of impression. Finally, an adaptive method based on wavelet extrema and the Henry system for core point detection is proposed. Experiments conducted using the FVC2002 DB1 and DB2 databases prove that our method can detect SPs reliably.
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Affiliation(s)
- Ngoc Tuyen Le
- Institute of Photonic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan
| | - Duc Huy Le
- Department of Electronic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan
| | - Jing-Wein Wang
- Institute of Photonic Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan
- Correspondence: ; Tel.: +886-930943143
| | - Chih-Chiang Wang
- Department of Computer Science and Information Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan
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