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Chen Y, Zhang X, Yang J, Han G, Zhang H, Lai M, Zhao J. HDB-Net: hierarchical dual-branch network for retinal layer segmentation in diseased OCT images. BIOMEDICAL OPTICS EXPRESS 2024; 15:5359-5383. [PMID: 39296382 PMCID: PMC11407236 DOI: 10.1364/boe.530469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/23/2024] [Revised: 07/12/2024] [Accepted: 07/29/2024] [Indexed: 09/21/2024]
Abstract
Optical coherence tomography (OCT) retinal layer segmentation is a critical procedure of the modern ophthalmic process, which can be used for diagnosis and treatment of diseases such as diabetic macular edema (DME) and multiple sclerosis (MS). Due to the difficulties of low OCT image quality, highly similar retinal interlayer morphology, and the uncertain presence, shape and size of lesions, the existing algorithms do not perform well. In this work, we design an HDB-Net network for retinal layer segmentation in diseased OCT images, which solves this problem by combining global and detailed features. First, the proposed network uses a Swin transformer and Res50 as a parallel backbone network, combined with the pyramid structure in UperNet, to extract global context and aggregate multi-scale information from images. Secondly, a feature aggregation module (FAM) is designed to extract global context information from the Swin transformer and local feature information from ResNet by introducing mixed attention mechanism. Finally, the boundary awareness and feature enhancement module (BA-FEM) is used to extract the retinal layer boundary information and topological order from the low-resolution features of the shallow layer. Our approach has been validated on two public datasets, and Dice scores were 87.61% and 92.44, respectively, both outperforming other state-of-the-art technologies.
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Affiliation(s)
- Yu Chen
- The School of Mechatronics Engineering, Harbin Institute of Technology , Harbin, Heilongjiang 150001, China
| | - XueHe Zhang
- The School of Mechatronics Engineering, Harbin Institute of Technology , Harbin, Heilongjiang 150001, China
| | - Jiahui Yang
- The School of Mechatronics Engineering, Harbin Institute of Technology , Harbin, Heilongjiang 150001, China
| | - Gang Han
- The School of Mechatronics Engineering, Harbin Institute of Technology , Harbin, Heilongjiang 150001, China
| | - He Zhang
- The School of Mechatronics Engineering, Harbin Institute of Technology , Harbin, Heilongjiang 150001, China
| | - MingZhu Lai
- The School of Mathematics and Statistics, Hainan Normal University, Haikou, Hainan 571158, China
| | - Jie Zhao
- The School of Mechatronics Engineering, Harbin Institute of Technology , Harbin, Heilongjiang 150001, China
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2
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Haghayegh F, Norouziazad A, Haghani E, Feygin AA, Rahimi RH, Ghavamabadi HA, Sadighbayan D, Madhoun F, Papagelis M, Felfeli T, Salahandish R. Revolutionary Point-of-Care Wearable Diagnostics for Early Disease Detection and Biomarker Discovery through Intelligent Technologies. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2400595. [PMID: 38958517 PMCID: PMC11423253 DOI: 10.1002/advs.202400595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/16/2024] [Revised: 06/19/2024] [Indexed: 07/04/2024]
Abstract
Early-stage disease detection, particularly in Point-Of-Care (POC) wearable formats, assumes pivotal role in advancing healthcare services and precision-medicine. Public benefits of early detection extend beyond cost-effectively promoting healthcare outcomes, to also include reducing the risk of comorbid diseases. Technological advancements enabling POC biomarker recognition empower discovery of new markers for various health conditions. Integration of POC wearables for biomarker detection with intelligent frameworks represents ground-breaking innovations enabling automation of operations, conducting advanced large-scale data analysis, generating predictive models, and facilitating remote and guided clinical decision-making. These advancements substantially alleviate socioeconomic burdens, creating a paradigm shift in diagnostics, and revolutionizing medical assessments and technology development. This review explores critical topics and recent progress in development of 1) POC systems and wearable solutions for early disease detection and physiological monitoring, as well as 2) discussing current trends in adoption of smart technologies within clinical settings and in developing biological assays, and ultimately 3) exploring utilities of POC systems and smart platforms for biomarker discovery. Additionally, the review explores technology translation from research labs to broader applications. It also addresses associated risks, biases, and challenges of widespread Artificial Intelligence (AI) integration in diagnostics systems, while systematically outlining potential prospects, current challenges, and opportunities.
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Affiliation(s)
- Fatemeh Haghayegh
- Laboratory of Advanced Biotechnologies for Health Assessments (Lab‐HA)Biomedical Engineering ProgramLassonde School of EngineeringYork UniversityTorontoM3J 1P3Canada
- Department of Electrical Engineering and Computer Science (EECS)Lassonde School of EngineeringYork UniversityTorontoONM3J 1P3Canada
| | - Alireza Norouziazad
- Laboratory of Advanced Biotechnologies for Health Assessments (Lab‐HA)Biomedical Engineering ProgramLassonde School of EngineeringYork UniversityTorontoM3J 1P3Canada
- Department of Electrical Engineering and Computer Science (EECS)Lassonde School of EngineeringYork UniversityTorontoONM3J 1P3Canada
| | - Elnaz Haghani
- Laboratory of Advanced Biotechnologies for Health Assessments (Lab‐HA)Biomedical Engineering ProgramLassonde School of EngineeringYork UniversityTorontoM3J 1P3Canada
- Department of Electrical Engineering and Computer Science (EECS)Lassonde School of EngineeringYork UniversityTorontoONM3J 1P3Canada
| | - Ariel Avraham Feygin
- Laboratory of Advanced Biotechnologies for Health Assessments (Lab‐HA)Biomedical Engineering ProgramLassonde School of EngineeringYork UniversityTorontoM3J 1P3Canada
- Department of Electrical Engineering and Computer Science (EECS)Lassonde School of EngineeringYork UniversityTorontoONM3J 1P3Canada
| | - Reza Hamed Rahimi
- Laboratory of Advanced Biotechnologies for Health Assessments (Lab‐HA)Biomedical Engineering ProgramLassonde School of EngineeringYork UniversityTorontoM3J 1P3Canada
- Department of Electrical Engineering and Computer Science (EECS)Lassonde School of EngineeringYork UniversityTorontoONM3J 1P3Canada
| | - Hamidreza Akbari Ghavamabadi
- Laboratory of Advanced Biotechnologies for Health Assessments (Lab‐HA)Biomedical Engineering ProgramLassonde School of EngineeringYork UniversityTorontoM3J 1P3Canada
- Department of Electrical Engineering and Computer Science (EECS)Lassonde School of EngineeringYork UniversityTorontoONM3J 1P3Canada
| | - Deniz Sadighbayan
- Department of BiologyFaculty of ScienceYork UniversityTorontoONM3J 1P3Canada
| | - Faress Madhoun
- Laboratory of Advanced Biotechnologies for Health Assessments (Lab‐HA)Biomedical Engineering ProgramLassonde School of EngineeringYork UniversityTorontoM3J 1P3Canada
- Department of Electrical Engineering and Computer Science (EECS)Lassonde School of EngineeringYork UniversityTorontoONM3J 1P3Canada
| | - Manos Papagelis
- Department of Electrical Engineering and Computer Science (EECS)Lassonde School of EngineeringYork UniversityTorontoONM3J 1P3Canada
| | - Tina Felfeli
- Department of Ophthalmology and Vision SciencesUniversity of TorontoOntarioM5T 3A9Canada
- Institute of Health PolicyManagement and EvaluationUniversity of TorontoOntarioM5T 3M6Canada
| | - Razieh Salahandish
- Laboratory of Advanced Biotechnologies for Health Assessments (Lab‐HA)Biomedical Engineering ProgramLassonde School of EngineeringYork UniversityTorontoM3J 1P3Canada
- Department of Electrical Engineering and Computer Science (EECS)Lassonde School of EngineeringYork UniversityTorontoONM3J 1P3Canada
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3
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Lin J, Han Y, Liu M, Wang X. Effects of Acute Mental Stress on Choroidal Thickness. Bioengineering (Basel) 2024; 11:684. [PMID: 39061766 PMCID: PMC11273856 DOI: 10.3390/bioengineering11070684] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2024] [Revised: 06/30/2024] [Accepted: 07/04/2024] [Indexed: 07/28/2024] Open
Abstract
Purpose: Previous studies have indicated an association between education and myopia, suggesting that numerous stress events during the educational process may influence eye health. This study aimed to investigate the impact of mental stress induced by mental arithmetic (MA) on choroidal thickness (CT). Methods: This study included 33 participants aged between 19 and 29 years. Swept-source optical coherence tomography (SS-OCT) was used to capture images of the posterior segment of the left eye during baseline and MA to assess changes in the CT. After denoising and compensation, the baseline images and MA images that had been rigidly registered and resampled to the baseline images were segmented using a deep learning-based method. Based on the segmentation results, the CT within the regions of 1 mm and 3 mm diameter centered at the lowest point of the fovea was calculated. Results: Significant increases were observed in both CT1mm and CT3mm during MA, with mean changes of 2.742 ± 7.098 μm (p = 0.034) and 3.326 ± 6.143 μm (p < 0.001), respectively. Conclusions: Thickening of the choroid has been observed during acute mental stress. We speculate that long-term or chronic mental stress could have a potential adverse impact on myopia progression.
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Affiliation(s)
- Jiechun Lin
- School of Ophthalmology and Optometry, School of Biomedical Engineering, Wenzhou Medical University, Wenzhou 325035, China; (J.L.); (M.L.)
- Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100083, China;
| | - Yingxiang Han
- Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100083, China;
| | - Meng Liu
- School of Ophthalmology and Optometry, School of Biomedical Engineering, Wenzhou Medical University, Wenzhou 325035, China; (J.L.); (M.L.)
- Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100083, China;
| | - Xiaofei Wang
- School of Ophthalmology and Optometry, School of Biomedical Engineering, Wenzhou Medical University, Wenzhou 325035, China; (J.L.); (M.L.)
- Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science and Medical Engineering, Beihang University, Beijing 100083, China;
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Liu X, Li X, Zhang Y, Wang M, Yao J, Tang J. Boundary-Repairing Dual-Path Network for Retinal Layer Segmentation in OCT Image with Pigment Epithelial Detachment. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01093-y. [PMID: 38740662 DOI: 10.1007/s10278-024-01093-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 03/05/2024] [Accepted: 03/06/2024] [Indexed: 05/16/2024]
Abstract
Automatic retinal layer segmentation in optical coherence tomography (OCT) images is crucial for the diagnosis of ocular diseases. Currently, automatic retinal layer segmentation works well with normal OCT images. However, pigment epithelial detachment (PED) dramatically alters the retinal structure, causing blurred boundaries and partial disappearance of the Bruch's Membrane (BM), thus posing challenges to the segmentation. To tackle these problems, we propose a novel dual-path U-shaped network for simultaneous layer segmentation and boundary regression. This network first designs a feature interaction fusion (FIF) module to strengthen the boundary shape constraints in the layer path. To address the challenge posed by partial BM disappearance and boundary-blurring, we propose a layer boundary repair (LBR) module. This module aims to use contrastive loss to enhance the confidence of blurred boundary regions and refine the segmentation of layer boundaries through the re-prediction head. In addition, we introduce a novel bilateral threshold distance map (BTDM) designed for the boundary path. The BTDM serves to emphasize information within boundary regions. This map, combined with the updated probability map, culminates in topology-guaranteed segmentation results achieved through a topology correction (TC) module. We investigated the proposed network on two severely deformed datasets (i.e., OCTA-500 and Aier-PED) and one slightly deformed dataset (i.e., DUKE). The proposed method achieves an average Dice score of 94.26% on the OCTA-500 dataset, which was 1.5% higher than BAU-Net and outperformed other methods. In the DUKE and Aier-PED datasets, the proposed method achieved average Dice scores of 91.65% and 95.75%, respectively.
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Affiliation(s)
- Xiaoming Liu
- School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, 430065, China.
- Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System, Wuhan, 430065, China.
| | - Xiao Li
- School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, 430065, China
- Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System, Wuhan, 430065, China
| | - Ying Zhang
- Wuhan Aier Eye Hospital of Wuhan University, Wuhan, China
| | - Man Wang
- Wuhan Aier Eye Hospital of Wuhan University, Wuhan, China
| | - Junping Yao
- Department of Ophthalmology, Tianyou Hospital Affiliated to Wuhan University of Science and Technology, Wuhan, China
| | - Jinshan Tang
- Department of Health Administration and Policy, College of Health and Human Services, George Mason University, Fairfax, VA, 22030, USA
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5
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Liu H, Gao W, Yang L, Wu D, Zhao D, Chen K, Liu J, Ye Y, Xu RX, Sun M. Semantic uncertainty Guided Cross-Transformer for enhanced macular edema segmentation in OCT images. Comput Biol Med 2024; 174:108458. [PMID: 38631114 DOI: 10.1016/j.compbiomed.2024.108458] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2023] [Revised: 03/03/2024] [Accepted: 04/07/2024] [Indexed: 04/19/2024]
Abstract
Macular edema, a prevalent ocular complication observed in various retinal diseases, can lead to significant vision loss or blindness, necessitating accurate and timely diagnosis. Despite the potential of deep learning for segmentation of macular edema, challenges persist in accurately identifying lesion boundaries, especially in low-contrast and noisy regions, and in distinguishing between Inner Retinal Fluid (IRF), Sub-Retinal Fluid (SRF), and Pigment Epithelial Detachment (PED) lesions. To address these challenges, we present a novel approach, termed Semantic Uncertainty Guided Cross-Transformer Network (SuGCTNet), for the simultaneous segmentation of multi-class macular edema. Our proposed method comprises two key components, the semantic uncertainty guided attention module (SuGAM) and the Cross-Transformer module (CTM). The SuGAM module utilizes semantic uncertainty to allocate additional attention to regions with semantic ambiguity, improves the segmentation performance of these challenging areas. On the other hand, the CTM module capitalizes on both uncertainty information and multi-scale image features to enhance the overall continuity of the segmentation process, effectively minimizing feature confusion among different lesion types. Rigorous evaluation on public datasets and various OCT imaging device data demonstrates the superior performance of our proposed method compared to state-of-the-art approaches, highlighting its potential as a valuable tool for improving the accuracy and reproducibility of macular edema segmentation in clinical settings, and ultimately aiding in the early detection and diagnosis of macular edema-related diseases and associated retinal conditions.
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Affiliation(s)
- Hui Liu
- Department of Precision Machinery and Instruments, University of Science and Technology of China, Hefei, Anhui, 230026, PR China; School of Biomedical Engineering, Division of Life and Medicine, University of Science and Technology of China, Hefei, Anhui, 230026, PR China; Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, Jiangsu, 215123, PR China
| | - Wenteng Gao
- Department of Precision Machinery and Instruments, University of Science and Technology of China, Hefei, Anhui, 230026, PR China
| | - Lei Yang
- Department of Precision Machinery and Instruments, University of Science and Technology of China, Hefei, Anhui, 230026, PR China
| | - Di Wu
- School of Biomedical Engineering, Division of Life and Medicine, University of Science and Technology of China, Hefei, Anhui, 230026, PR China
| | - Dehan Zhao
- Department of Precision Machinery and Instruments, University of Science and Technology of China, Hefei, Anhui, 230026, PR China
| | - Kun Chen
- Department of Precision Machinery and Instruments, University of Science and Technology of China, Hefei, Anhui, 230026, PR China
| | - Jicheng Liu
- School of Biomedical Engineering, Division of Life and Medicine, University of Science and Technology of China, Hefei, Anhui, 230026, PR China; Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, Jiangsu, 215123, PR China
| | - Yu Ye
- Nanjing Research Institute of Electronics Technology, Nanjing, Jiangsu, 210039, PR China
| | - Ronald X Xu
- School of Biomedical Engineering, Division of Life and Medicine, University of Science and Technology of China, Hefei, Anhui, 230026, PR China; Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, Jiangsu, 215123, PR China.
| | - Mingzhai Sun
- School of Biomedical Engineering, Division of Life and Medicine, University of Science and Technology of China, Hefei, Anhui, 230026, PR China; Suzhou Institute for Advanced Research, University of Science and Technology of China, Suzhou, Jiangsu, 215123, PR China.
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6
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Orosco FL. African swine fever virus proteins against host antiviral innate immunity and their implications for vaccine development. Open Vet J 2024; 14:941-951. [PMID: 38808296 PMCID: PMC11128636 DOI: 10.5455/ovj.2024.v14.i4.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Accepted: 03/13/2024] [Indexed: 05/30/2024] Open
Abstract
African swine fever virus (ASFV) poses a significant threat to global swine populations, necessitating a profound understanding of viral strategies against host antiviral innate immunity. This review synthesizes current knowledge regarding ASFV proteins and their intricate interactions with host defenses. Noteworthy findings encompass the modulation of interferon signaling, manipulation of inflammatory pathways, and the impact on cellular apoptosis. The implications of these findings provide a foundation for advancing vaccine strategies against ASFV. In conclusion, this review consolidates current knowledge, emphasizing the adaptability of ASFV in subverting host immunity. Identified research gaps underscore the need for continued exploration, presenting opportunities for developing targeted vaccines. This synthesis provides a roadmap for future investigations, aiming to enhance our preparedness against the devastating impact of ASFV on global swine populations.
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Affiliation(s)
- Fredmoore L. Orosco
- Virology and Vaccine Institute of the Philippines Program, Industrial Technology Development Institute, Department of Science and Technology, Taguig, Philippines
- Department of Biology, College of Arts and Sciences, University of the Philippines Manila, Metro Manila, Philippines
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7
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Wang Y, Yang Z, Liu X, Li Z, Wu C, Wang Y, Jin K, Chen D, Jia G, Chen X, Ye J, Huang X. PGKD-Net: Prior-guided and Knowledge Diffusive Network for Choroid Segmentation. Artif Intell Med 2024; 150:102837. [PMID: 38553151 DOI: 10.1016/j.artmed.2024.102837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2023] [Revised: 03/01/2024] [Accepted: 03/03/2024] [Indexed: 04/02/2024]
Abstract
The thickness of the choroid is considered to be an important indicator of clinical diagnosis. Therefore, accurate choroid segmentation in retinal OCT images is crucial for monitoring various ophthalmic diseases. However, this is still challenging due to the blurry boundaries and interference from other lesions. To address these issues, we propose a novel prior-guided and knowledge diffusive network (PGKD-Net) to fully utilize retinal structural information to highlight choroidal region features and boost segmentation performance. Specifically, it is composed of two parts: a Prior-mask Guided Network (PG-Net) for coarse segmentation and a Knowledge Diffusive Network (KD-Net) for fine segmentation. In addition, we design two novel feature enhancement modules, Multi-Scale Context Aggregation (MSCA) and Multi-Level Feature Fusion (MLFF). The MSCA module captures the long-distance dependencies between features from different receptive fields and improves the model's ability to learn global context. The MLFF module integrates the cascaded context knowledge learned from PG-Net to benefit fine-level segmentation. Comprehensive experiments are conducted to evaluate the performance of the proposed PGKD-Net. Experimental results show that our proposed method achieves superior segmentation accuracy over other state-of-the-art methods. Our code is made up publicly available at: https://github.com/yzh-hdu/choroid-segmentation.
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Affiliation(s)
- Yaqi Wang
- College of Media Engineering, Communication University of Zhejiang, Hangzhou, China.
| | - Zehua Yang
- Hangzhou Dianzi University, Hangzhou, China.
| | - Xindi Liu
- Department of Ophthalmology, School of Medicine, The Second Affiliated Hospital of Zhejiang University, Hangzhou, China.
| | - Zhi Li
- Hangzhou Dianzi University, Hangzhou, China.
| | - Chengyu Wu
- Department of Mechanical, Electrical and Information Engineering, Shandong University, Weihai, China.
| | - Yizhen Wang
- Hangzhou Dianzi University, Hangzhou, China.
| | - Kai Jin
- Department of Ophthalmology, School of Medicine, The Second Affiliated Hospital of Zhejiang University, Hangzhou, China.
| | - Dechao Chen
- Hangzhou Dianzi University, Hangzhou, China.
| | | | | | - Juan Ye
- Department of Ophthalmology, School of Medicine, The Second Affiliated Hospital of Zhejiang University, Hangzhou, China.
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8
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Cao G, Wu Y, Peng Z, Zhou Z, Dai C. Self-attention CNN for retinal layer segmentation in OCT. BIOMEDICAL OPTICS EXPRESS 2024; 15:1605-1617. [PMID: 38495698 PMCID: PMC10942697 DOI: 10.1364/boe.510464] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Revised: 01/13/2024] [Accepted: 01/30/2024] [Indexed: 03/19/2024]
Abstract
The structure of the retinal layers provides valuable diagnostic information for many ophthalmic diseases. Optical coherence tomography (OCT) obtains cross-sectional images of the retina, which reveals information about the retinal layers. The U-net based approaches are prominent in retinal layering methods, which are usually beneficial to local characteristics but not good at obtaining long-distance dependence for contextual information. Furthermore, the morphology of retinal layers with the disease is more complex, which brings more significant challenges to the task of retinal layer segmentation. We propose a U-shaped network combining an encoder-decoder architecture and self-attention mechanisms. In response to the characteristics of retinal OCT cross-sectional images, a self-attentive module in the vertical direction is added to the bottom of the U-shaped network, and an attention mechanism is also added in skip connection and up-sampling to enhance essential features. In this method, the transformer's self-attentive mechanism obtains the global field of perception, thus providing the missing context information for convolutions, and the convolutional neural network also efficiently extracts local features, compensating the local details the transformer ignores. The experiment results showed that our method is accurate and better than other methods for segmentation of the retinal layers, with the average Dice scores of 0.871 and 0.820, respectively, on two public retinal OCT image datasets. To perform the layer segmentation of retinal OCT image better, the proposed method incorporates the transformer's self-attention mechanism in a U-shaped network, which is helpful for ophthalmic disease diagnosis.
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Affiliation(s)
- Guogang Cao
- Shanghai Institute of Technology, Shanghai 201418, China
| | - Yan Wu
- Shanghai Institute of Technology, Shanghai 201418, China
| | - Zeyu Peng
- Shanghai Institute of Technology, Shanghai 201418, China
| | - Zhilin Zhou
- Shanghai Institute of Technology, Shanghai 201418, China
| | - Cuixia Dai
- Shanghai Institute of Technology, Shanghai 201418, China
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9
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Fang J, Xing A, Chen Y, Zhou F. SeqCorr-EUNet: A sequence correction dual-flow network for segmentation and quantification of anterior segment OCT image. Comput Biol Med 2024; 171:108143. [PMID: 38364662 DOI: 10.1016/j.compbiomed.2024.108143] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2023] [Revised: 01/16/2024] [Accepted: 02/12/2024] [Indexed: 02/18/2024]
Abstract
The accurate segmentation of AS-OCT images is a prerequisite for the morphological details analysis of anterior segment structure and the extraction of clinical biological parameters, which play an essential role in the diagnosis, evaluation, and preoperative prognosis management of many ophthalmic diseases. Manually marking the boundaries of the anterior segment tissue is time-consuming and error-prone, with inherent speckle noise, various artifacts, and some low-quality scanned images further increasing the difficulty of the segmentation task. In this work, we propose a novel model called SeqCorr-EUNet with a dual-flow architecture based on convolutional gated recursive sequence correction for semantic segmentation and quantification of AS-OCT images. An EfficientNet encoder is employed to enhance the intra-slice features extraction ability of semantic segmentation flow. The sequence correction flow based on ConvGRU is introduced to extract inter-slice features from consecutive adjacent slices. Spatio-temporal information is fused to correct the morphological details of pre-segmentation results. And the channel attention gate is inserted into the skip-connection between encoder and decoder to enrich the contextual information and suppress the noise of irrelevant regions. Based on the segmentation results of the anterior segment structures, we achieved automatic extraction of essential clinical parameters, 3D reconstruction of the anterior chamber structure, and measurement of anterior chamber volume. The proposed SeqCorr-EUNet has been evaluated on the public AS-OCT dataset. The experimental results show that our method is competitive compared with the existing methods and significantly improves the segmentation and quantification performance of low-quality imaging structures in AS-OCT images.
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Affiliation(s)
- Jing Fang
- School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, 230601, Anhui, China.
| | - Aoyu Xing
- School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, 230601, Anhui, China.
| | - Ying Chen
- Department of Ophthalmology, Hospital of University of Science and Technology of China, Hefei, 230026, Anhui, China.
| | - Fang Zhou
- School of Computer Science and Information Engineering, Hefei University of Technology, Hefei, 230601, Anhui, China.
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10
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Liu H, Wei D, Lu D, Tang X, Wang L, Zheng Y. Simultaneous alignment and surface regression using hybrid 2D-3D networks for 3D coherent layer segmentation of retinal OCT images with full and sparse annotations. Med Image Anal 2024; 91:103019. [PMID: 37944431 DOI: 10.1016/j.media.2023.103019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 08/28/2023] [Accepted: 10/31/2023] [Indexed: 11/12/2023]
Abstract
Layer segmentation is important to quantitative analysis of retinal optical coherence tomography (OCT). Recently, deep learning based methods have been developed to automate this task and yield remarkable performance. However, due to the large spatial gap and potential mismatch between the B-scans of an OCT volume, all of them were based on 2D segmentation of individual B-scans, which may lose the continuity and diagnostic information of the retinal layers in 3D space. Besides, most of these methods required dense annotation of the OCT volumes, which is labor-intensive and expertise-demanding. This work presents a novel framework based on hybrid 2D-3D convolutional neural networks (CNNs) to obtain continuous 3D retinal layer surfaces from OCT volumes, which works well with both full and sparse annotations. The 2D features of individual B-scans are extracted by an encoder consisting of 2D convolutions. These 2D features are then used to produce the alignment displacement vectors and layer segmentation by two 3D decoders coupled via a spatial transformer module. Two losses are proposed to utilize the retinal layers' natural property of being smooth for B-scan alignment and layer segmentation, respectively, and are the key to the semi-supervised learning with sparse annotation. The entire framework is trained end-to-end. To the best of our knowledge, this is the first work that attempts 3D retinal layer segmentation in volumetric OCT images based on CNNs. Experiments on a synthetic dataset and three public clinical datasets show that our framework can effectively align the B-scans for potential motion correction, and achieves superior performance to state-of-the-art 2D deep learning methods in terms of both layer segmentation accuracy and cross-B-scan 3D continuity in both fully and semi-supervised settings, thus offering more clinical values than previous works.
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Affiliation(s)
- Hong Liu
- School of Informatics, Xiamen University, Xiamen 361005, China; National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361005, China; Jarvis Research Center, Tencent YouTu Lab, Shenzhen 518075, China
| | - Dong Wei
- Jarvis Research Center, Tencent YouTu Lab, Shenzhen 518075, China
| | - Donghuan Lu
- Jarvis Research Center, Tencent YouTu Lab, Shenzhen 518075, China
| | - Xiaoying Tang
- Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Liansheng Wang
- School of Informatics, Xiamen University, Xiamen 361005, China.
| | - Yefeng Zheng
- Jarvis Research Center, Tencent YouTu Lab, Shenzhen 518075, China
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11
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Xu Y, Yang W. Editorial: Artificial intelligence applications in chronic ocular diseases. Front Cell Dev Biol 2023; 11:1295850. [PMID: 38143924 PMCID: PMC10740206 DOI: 10.3389/fcell.2023.1295850] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2023] [Accepted: 11/28/2023] [Indexed: 12/26/2023] Open
Affiliation(s)
- Yanwu Xu
- School of Future Technology, South China University of Technology, Guangzhou, Guangdong Province, China
- Pazhou Lab, Guangzhou, Guangdong Province, China
| | - Weihua Yang
- Shenzhen Eye Institute, Shenzhen Eye Hospital, Jinan University, Shenzhen, Guangdong Province, China
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12
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Gende M, Mallen V, de Moura J, Cordon B, Garcia-Martin E, Sanchez CI, Novo J, Ortega M. Automatic Segmentation of Retinal Layers in Multiple Neurodegenerative Disorder Scenarios. IEEE J Biomed Health Inform 2023; 27:5483-5494. [PMID: 37682646 DOI: 10.1109/jbhi.2023.3313392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/10/2023]
Abstract
Retinal Optical Coherence Tomography (OCT) allows the non-invasive direct observation of the central nervous system, enabling the measurement and extraction of biomarkers from neural tissue that can be helpful in the assessment of ocular, systemic and Neurological Disorders (ND). Deep learning models can be trained to segment the retinal layers for biomarker extraction. However, the onset of ND can have an impact on the neural tissue, which can lead to the degraded performance of models not exposed to images displaying signs of disease during training. We present a fully automatic approach for the retinal layer segmentation in multiple neurodegenerative disorder scenarios, using an annotated dataset of patients of the most prevalent NDs: Alzheimer's disease, Parkinson's disease, multiple sclerosis and essential tremor, along with healthy control patients. Furthermore, we present a two-part, comprehensive study on the effects of ND on the performance of these models. The results show that images of healthy patients may not be sufficient for the robust training of automated segmentation models intended for the analysis of ND patients, and that using images representative of different NDs can increase the model performance. These results indicate that the presence or absence of patients of ND in datasets should be taken into account when training deep learning models for retinal layer segmentation, and that the proposed approach can provide a valuable tool for the robust and reliable diagnosis in multiple scenarios of ND.
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Sampath Kumar A, Schlosser T, Langner H, Ritter M, Kowerko D. Improving OCT Image Segmentation of Retinal Layers by Utilizing a Machine Learning Based Multistage System of Stacked Multiscale Encoders and Decoders. Bioengineering (Basel) 2023; 10:1177. [PMID: 37892907 PMCID: PMC10603937 DOI: 10.3390/bioengineering10101177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2023] [Revised: 10/02/2023] [Accepted: 10/05/2023] [Indexed: 10/29/2023] Open
Abstract
Optical coherence tomography (OCT)-based retinal imagery is often utilized to determine influential factors in patient progression and treatment, for which the retinal layers of the human eye are investigated to assess a patient's health status and eyesight. In this contribution, we propose a machine learning (ML)-based multistage system of stacked multiscale encoders and decoders for the image segmentation of OCT imagery of the retinal layers to enable the following evaluation regarding the physiological and pathological states. Our proposed system's results highlight its benefits compared to currently investigated approaches by combining commonly deployed methods from deep learning (DL) while utilizing deep neural networks (DNN). We conclude that by stacking multiple multiscale encoders and decoders, improved scores for the image segmentation task can be achieved. Our retinal-layer-based segmentation results in a final segmentation performance of up to 82.25±0.74% for the Sørensen-Dice coefficient, outperforming the current best single-stage model by 1.55% with a score of 80.70±0.20%, given the evaluated peripapillary OCT data set. Additionally, we provide results on the data sets Duke SD-OCT, Heidelberg, and UMN to illustrate our model's performance on especially noisy data sets.
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Affiliation(s)
- Arunodhayan Sampath Kumar
- Junior Professorship of Media Computing, Chemnitz University of Technology, 09107 Chemnitz, Germany; (A.S.K.); (T.S.)
| | - Tobias Schlosser
- Junior Professorship of Media Computing, Chemnitz University of Technology, 09107 Chemnitz, Germany; (A.S.K.); (T.S.)
| | - Holger Langner
- Professorship of Media Informatics, University of Applied Sciences Mittweida, 09648 Mittweida, Germany; (H.L.); (M.R.)
| | - Marc Ritter
- Professorship of Media Informatics, University of Applied Sciences Mittweida, 09648 Mittweida, Germany; (H.L.); (M.R.)
| | - Danny Kowerko
- Junior Professorship of Media Computing, Chemnitz University of Technology, 09107 Chemnitz, Germany; (A.S.K.); (T.S.)
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14
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Xue H, Fang Q, Yao Y, Teng Y. 3D PET/CT tumor segmentation based on nnU-Net with GCN refinement. Phys Med Biol 2023; 68:185018. [PMID: 37549672 DOI: 10.1088/1361-6560/acede6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Accepted: 08/07/2023] [Indexed: 08/09/2023]
Abstract
Objective. Whole-body positron emission tomography/computed tomography (PET/CT) scans are an important tool for diagnosing various malignancies (e.g. malignant melanoma, lymphoma, or lung cancer), and accurate segmentation of tumors is a key part of subsequent treatment. In recent years, convolutional neural network based segmentation methods have been extensively investigated. However, these methods often give inaccurate segmentation results, such as oversegmentation and undersegmentation. To address these issues, we propose a postprocessing method based on a graph convolutional network (GCN) to refine inaccurate segmentation results and improve the overall segmentation accuracy.Approach. First, nnU-Net is used as an initial segmentation framework, and the uncertainty in the segmentation results is analyzed. Certain and uncertain pixels are used to establish the nodes of a graph. Each node and its 6 neighbors form an edge, and 32 nodes are randomly selected as uncertain nodes to form edges. The highly uncertain nodes are used as the subsequent refinement targets. Second, the nnU-Net results of the certain nodes are used as labels to form a semisupervised graph network problem, and the uncertain part is optimized by training the GCN to improve the segmentation performance. This describes our proposed nnU-Net + GCN segmentation framework.Main results.We perform tumor segmentation experiments with the PET/CT dataset from the MICCIA2022 autoPET challenge. Among these data, 30 cases are randomly selected for testing, and the experimental results show that the false-positive rate is effectively reduced with nnU-Net + GCN refinement. In quantitative analysis, there is an improvement of 2.1% for the average Dice score, 6.4 for the 95% Hausdorff distance (HD95), and 1.7 for the average symmetric surface distance.Significance. The quantitative and qualitative evaluation results show that GCN postprocessing methods can effectively improve the tumor segmentation performance.
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Affiliation(s)
- Hengzhi Xue
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110004, People's Republic of China
| | - Qingqing Fang
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110004, People's Republic of China
| | - Yudong Yao
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110004, People's Republic of China
- Department of Electrical and Computer Engineering, Steven Institute of Technology, Hoboken, NJ 07102, United States of America
| | - Yueyang Teng
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110004, People's Republic of China
- Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Shenyang, 110169, People's Republic of China
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15
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Zheng Q, Li Z, Zhang J, Mei C, Li G, Wang L. Automated segmentation of palpebral fissures from eye videography using a texture fusion neural network. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104820] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/22/2023]
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16
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He X, Wang Y, Poiesi F, Song W, Xu Q, Feng Z, Wan Y. Exploiting multi-granularity visual features for retinal layer segmentation in human eyes. Front Bioeng Biotechnol 2023; 11:1191803. [PMID: 37324431 PMCID: PMC10267414 DOI: 10.3389/fbioe.2023.1191803] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 05/22/2023] [Indexed: 06/17/2023] Open
Abstract
Accurate segmentation of retinal layer boundaries can facilitate the detection of patients with early ophthalmic disease. Typical segmentation algorithms operate at low resolutions without fully exploiting multi-granularity visual features. Moreover, several related studies do not release their datasets that are key for the research on deep learning-based solutions. We propose a novel end-to-end retinal layer segmentation network based on ConvNeXt, which can retain more feature map details by using a new depth-efficient attention module and multi-scale structures. In addition, we provide a semantic segmentation dataset containing 206 retinal images of healthy human eyes (named NR206 dataset), which is easy to use as it does not require any additional transcoding processing. We experimentally show that our segmentation approach outperforms state-of-the-art approaches on this new dataset, achieving, on average, a Dice score of 91.3% and mIoU of 84.4%. Moreover, our approach achieves state-of-the-art performance on a glaucoma dataset and a diabetic macular edema (DME) dataset, showing that our model is also suitable for other applications. We will make our source code and the NR206 dataset publicly available at (https://github.com/Medical-Image-Analysis/Retinal-layer-segmentation).
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Affiliation(s)
- Xiang He
- School of Mechanical Engineering, Shandong University, Jinan, China
- Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, China
| | | | | | - Weiye Song
- School of Mechanical Engineering, Shandong University, Jinan, China
| | - Quanqing Xu
- School of Mechanical Engineering, Shandong University, Jinan, China
| | - Zixuan Feng
- School of Mechanical Engineering, Shandong University, Jinan, China
| | - Yi Wan
- School of Mechanical Engineering, Shandong University, Jinan, China
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17
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Lu Y, Shen Y, Xing X, Ye C, Meng MQH. Boundary-enhanced semi-supervised retinal layer segmentation in optical coherence tomography images using fewer labels. Comput Med Imaging Graph 2023; 105:102199. [PMID: 36805709 DOI: 10.1016/j.compmedimag.2023.102199] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Revised: 01/24/2023] [Accepted: 02/06/2023] [Indexed: 02/13/2023]
Abstract
Automatic segmentation of multiple layers in retinal optical coherence tomography (OCT) images is crucial for eye disease diagnosis and treatment. Despite the success of deep learning algorithms, it still remains a challenge due to the blurry layer boundaries and lack of adequate pixel-wise annotations. To tackle these issues, we propose a Boundary-Enhanced Semi-supervised Network (BE-SemiNet) that exploits an auxiliary distance map regression task to improve retinal layer segmentation with scarce labeled data and abundant unlabeled data. Specifically, a novel Unilaterally Truncated Distance Map (UTDM) is firstly introduced to alleviate the class imbalance problem and enhance the layer boundary learning in the regression task. Then for the pixel-wise segmentation and UTDM regression branches, we impose task-level and data-level consistency regularization on unlabeled data to enrich the diversity of unsupervised information and improve the regularization effects. Pseudo supervision is incorporated in consistency regularization to bridge the task prediction spaces for consistency and expand training labeled data. Experiments on two public retinal OCT datasets show that our method can greatly improve the supervised baseline performance with only 5 annotations and outperform the state-of-the-art methods. Since it is difficult and labor-expensive to obtain adequate pixel-wise annotations in practice, our method has a promising application future in clinical retinal OCT image analysis.
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Affiliation(s)
- Ye Lu
- Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, China
| | - Yutian Shen
- Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, China
| | - Xiaohan Xing
- Department of Electrical Engineering, The City University of Hong Kong, Hong Kong, China
| | - Chengwei Ye
- Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, China
| | - Max Q-H Meng
- Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong, China; Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, China; Shenzhen Research Institute of The Chinese University of Hong Kong, Shenzhen, China.
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18
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Wei X, Sui R. A Review of Machine Learning Algorithms for Retinal Cyst Segmentation on Optical Coherence Tomography. SENSORS (BASEL, SWITZERLAND) 2023; 23:3144. [PMID: 36991857 PMCID: PMC10054815 DOI: 10.3390/s23063144] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Revised: 03/02/2023] [Accepted: 03/07/2023] [Indexed: 06/19/2023]
Abstract
Optical coherence tomography (OCT) is an emerging imaging technique for diagnosing ophthalmic diseases and the visual analysis of retinal structure changes, such as exudates, cysts, and fluid. In recent years, researchers have increasingly focused on applying machine learning algorithms, including classical machine learning and deep learning methods, to automate retinal cysts/fluid segmentation. These automated techniques can provide ophthalmologists with valuable tools for improved interpretation and quantification of retinal features, leading to more accurate diagnosis and informed treatment decisions for retinal diseases. This review summarized the state-of-the-art algorithms for the three essential steps of cyst/fluid segmentation: image denoising, layer segmentation, and cyst/fluid segmentation, while emphasizing the significance of machine learning techniques. Additionally, we provided a summary of the publicly available OCT datasets for cyst/fluid segmentation. Furthermore, the challenges, opportunities, and future directions of artificial intelligence (AI) in OCT cyst segmentation are discussed. This review is intended to summarize the key parameters for the development of a cyst/fluid segmentation system and the design of novel segmentation algorithms and has the potential to serve as a valuable resource for imaging researchers in the development of assessment systems related to ocular diseases exhibiting cyst/fluid in OCT imaging.
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19
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Huang Z, Zheng H, Huang J, Yang Y, Wu Y, Ge L, Wang L. The Construction and Evaluation of a Multi-Task Convolutional Neural Network for a Cone-Beam Computed-Tomography-Based Assessment of Implant Stability. Diagnostics (Basel) 2022; 12:2673. [PMID: 36359516 PMCID: PMC9689694 DOI: 10.3390/diagnostics12112673] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Revised: 10/21/2022] [Accepted: 10/29/2022] [Indexed: 07/21/2023] Open
Abstract
Objectives: Assessing implant stability is integral to dental implant therapy. This study aimed to construct a multi-task cascade convolution neural network to evaluate implant stability using cone-beam computed tomography (CBCT). Methods: A dataset of 779 implant coronal section images was obtained from CBCT scans, and matching clinical information was used for the training and test datasets. We developed a multi-task cascade network based on CBCT to assess implant stability. We used the MobilenetV2-DeeplabV3+ semantic segmentation network, combined with an image processing algorithm in conjunction with prior knowledge, to generate the volume of interest (VOI) that was eventually used for the ResNet-50 classification of implant stability. The performance of the multitask cascade network was evaluated in a test set by comparing the implant stability quotient (ISQ), measured using an Osstell device. Results: The cascade network established in this study showed good prediction performance for implant stability classification. The binary, ternary, and quaternary ISQ classification test set accuracies were 96.13%, 95.33%, and 92.90%, with mean precisions of 96.20%, 95.33%, and 93.71%, respectively. In addition, this cascade network evaluated each implant's stability in only 3.76 s, indicating high efficiency. Conclusions: To our knowledge, this is the first study to present a CBCT-based deep learning approach CBCT to assess implant stability. The multi-task cascade network accomplishes a series of tasks related to implant denture segmentation, VOI extraction, and implant stability classification, and has good concordance with the ISQ.
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Affiliation(s)
- Zelun Huang
- Guangzhou Key Laboratory of Basic and Applied Research of Oral Regenerative Medicine, Guangdong Engineering Research Center of Oral Restoration and Reconstruction, Affiliated Stomatology Hospital of Guangzhou Medical University, Guangzhou 510182, China
| | - Haoran Zheng
- Department of Chemical & Materials Engineering, University of Auckland, Auckland 1010, New Zealand
| | - Junqiang Huang
- Department of Stomatology, The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou 510150, China
| | - Yang Yang
- Guangzhou Key Laboratory of Basic and Applied Research of Oral Regenerative Medicine, Guangdong Engineering Research Center of Oral Restoration and Reconstruction, Affiliated Stomatology Hospital of Guangzhou Medical University, Guangzhou 510182, China
| | - Yupeng Wu
- Guangzhou Key Laboratory of Basic and Applied Research of Oral Regenerative Medicine, Guangdong Engineering Research Center of Oral Restoration and Reconstruction, Affiliated Stomatology Hospital of Guangzhou Medical University, Guangzhou 510182, China
| | - Linhu Ge
- Guangzhou Key Laboratory of Basic and Applied Research of Oral Regenerative Medicine, Guangdong Engineering Research Center of Oral Restoration and Reconstruction, Affiliated Stomatology Hospital of Guangzhou Medical University, Guangzhou 510182, China
| | - Liping Wang
- Guangzhou Key Laboratory of Basic and Applied Research of Oral Regenerative Medicine, Guangdong Engineering Research Center of Oral Restoration and Reconstruction, Affiliated Stomatology Hospital of Guangzhou Medical University, Guangzhou 510182, China
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20
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Wang Y, Galang C, Freeman WR, Nguyen TQ, An C. JOINT MOTION CORRECTION AND 3D SEGMENTATION WITH GRAPH-ASSISTED NEURAL NETWORKS FOR RETINAL OCT. PROCEEDINGS. INTERNATIONAL CONFERENCE ON IMAGE PROCESSING 2022; 2022:766-770. [PMID: 37342228 PMCID: PMC10280808 DOI: 10.1109/icip46576.2022.9898072] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/22/2023]
Abstract
Optical Coherence Tomography (OCT) is a widely used non-invasive high resolution 3D imaging technique for biological tissues and plays an important role in ophthalmology. OCT retinal layer segmentation is a fundamental image processing step for OCT-Angiography projection, and disease analysis. A major problem in retinal imaging is the motion artifacts introduced by involuntary eye movements. In this paper, we propose neural networks that jointly correct eye motion and retinal layer segmentation utilizing 3D OCT information, so that the segmentation among neighboring B-scans would be consistent. The experimental results show both visual and quantitative improvements by combining motion correction and 3D OCT layer segmentation comparing to conventional and deep-learning based 2D OCT layer segmentation.
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Affiliation(s)
- Yiqian Wang
- Department of Electrical and Computer Engineering, University of California, San Diego
| | - Carlo Galang
- Jacobs Retina Center, Shiley Eye Institute, University of California, San Diego
| | - William R Freeman
- Jacobs Retina Center, Shiley Eye Institute, University of California, San Diego
| | - Truong Q Nguyen
- Department of Electrical and Computer Engineering, University of California, San Diego
| | - Cheolhong An
- Department of Electrical and Computer Engineering, University of California, San Diego
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21
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Zhou H, Liu J, Laiginhas R, Zhang Q, Cheng Y, Zhang Y, Shi Y, Shen M, Gregori G, Rosenfeld PJ, Wang RK. Depth-resolved visualization and automated quantification of hyperreflective foci on OCT scans using optical attenuation coefficients. BIOMEDICAL OPTICS EXPRESS 2022; 13:4175-4189. [PMID: 36032584 PMCID: PMC9408241 DOI: 10.1364/boe.467623] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/13/2022] [Revised: 06/25/2022] [Accepted: 06/25/2022] [Indexed: 05/11/2023]
Abstract
An automated depth-resolved algorithm using optical attenuation coefficients (OACs) was developed to visualize, localize, and quantify hyperreflective foci (HRF) seen on OCT imaging that are associated with macular hyperpigmentation and represent an increased risk of disease progression in age related macular degeneration. To achieve this, we first transformed the OCT scans to linear representation, which were then contrasted by OACs. HRF were visualized and localized within the entire scan by differentiating HRF within the retina from HRF along the retinal pigment epithelium (RPE). The total pigment burden was quantified using the en face sum projection of an OAC slab between the inner limiting membrane (ILM) to Bruch's membrane (BM). The manual total pigment burden measurements were also obtained by combining manual outlines of HRF in the B-scans with the total area of hypotransmission defects outlined on sub-RPE slabs, which was used as the reference to compare with those obtained from the automated algorithm. 6×6 mm swept-source OCT scans were collected from a total of 49 eyes from 42 patients with macular HRF. We demonstrate that the algorithm was able to automatically distinguish between HRF within the retina and HRF along the RPE. In 24 test eyes, the total pigment burden measurements by the automated algorithm were compared with measurements obtained from manual segmentations. A significant correlation was found between the total pigment area measurements from the automated and manual segmentations (P < 0.001). The proposed automated algorithm based on OACs should be useful in studying eye diseases involving HRF.
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Affiliation(s)
- Hao Zhou
- Department of Bioengineering, University of Washington, Seattle, WA 98105, USA
| | - Jeremy Liu
- Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Rita Laiginhas
- Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Qinqin Zhang
- Department of Bioengineering, University of Washington, Seattle, WA 98105, USA
| | - Yuxuan Cheng
- Department of Bioengineering, University of Washington, Seattle, WA 98105, USA
| | - Yi Zhang
- Department of Bioengineering, University of Washington, Seattle, WA 98105, USA
| | - Yingying Shi
- Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Mengxi Shen
- Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Giovanni Gregori
- Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Philip J. Rosenfeld
- Department of Ophthalmology, Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, FL 33136, USA
| | - Ruikang K. Wang
- Department of Bioengineering, University of Washington, Seattle, WA 98105, USA
- Karalis Johnson Retina Center, Department of Ophthalmology, University of Washington, Seattle, WA 98105, USA
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22
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Marques R, Andrade De Jesus D, Barbosa-Breda J, Van Eijgen J, Stalmans I, van Walsum T, Klein S, G Vaz P, Sánchez Brea L. Automatic Segmentation of the Optic Nerve Head Region in Optical Coherence Tomography: A Methodological Review. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 220:106801. [PMID: 35429812 DOI: 10.1016/j.cmpb.2022.106801] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 03/07/2022] [Accepted: 04/01/2022] [Indexed: 06/14/2023]
Abstract
The optic nerve head (ONH) represents the intraocular section of the optic nerve, which is prone to damage by intraocular pressure (IOP). The advent of optical coherence tomography (OCT) has enabled the evaluation of novel ONH parameters, namely the depth and curvature of the lamina cribrosa (LC). Together with the Bruch's membrane minimum-rim-width (BMO-MRW), these seem to be promising ONH parameters for diagnosis and monitoring of retinal diseases such as glaucoma. Nonetheless, these OCT derived biomarkers are mostly extracted through manual segmentation, which is time-consuming and prone to bias, thus limiting their usability in clinical practice. The automatic segmentation of ONH in OCT scans could further improve the current clinical management of glaucoma and other diseases. This review summarizes the current state-of-the-art in automatic segmentation of the ONH in OCT. PubMed and Scopus were used to perform a systematic review. Additional works from other databases (IEEE, Google Scholar and ARVO IOVS) were also included, resulting in a total of 29 reviewed studies. For each algorithm, the methods, the size and type of dataset used for validation, and the respective results were carefully analysed. The results show a lack of consensus regarding the definition of segmented regions, extracted parameters and validation approaches, highlighting the importance and need of standardized methodologies for ONH segmentation. Only with a concrete set of guidelines, these automatic segmentation algorithms will build trust in data-driven segmentation models and be able to enter clinical practice.
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Affiliation(s)
- Rita Marques
- Laboratory for Instrumentation, Biomedical Engineering and Radiation Physics (LIBPhys-UC), Department of Physics, University of Coimbra, Coimbra, Portugal; Biomedical Imaging Group Rotterdam, Department of Radiology & Nuclear Medicine, Erasmus MC, Rotterdam, Netherlands
| | - Danilo Andrade De Jesus
- Biomedical Imaging Group Rotterdam, Department of Radiology & Nuclear Medicine, Erasmus MC, Rotterdam, Netherlands.
| | - João Barbosa-Breda
- Research Group Ophthalmology, Department of Neurosciences, KU Leuven, Leuven, Belgium; Cardiovascular R&D Center, Faculty of Medicine of the University of Porto, Porto, Portugal; Ophthalmology Department, São João Universitary Hospital Center, Porto, Portugal
| | - Jan Van Eijgen
- Research Group Ophthalmology, Department of Neurosciences, KU Leuven, Leuven, Belgium; Department of Ophthalmology, University Hospitals UZ Leuven, Leuven, Belgium
| | - Ingeborg Stalmans
- Research Group Ophthalmology, Department of Neurosciences, KU Leuven, Leuven, Belgium; Department of Ophthalmology, University Hospitals UZ Leuven, Leuven, Belgium
| | - Theo van Walsum
- Biomedical Imaging Group Rotterdam, Department of Radiology & Nuclear Medicine, Erasmus MC, Rotterdam, Netherlands
| | - Stefan Klein
- Biomedical Imaging Group Rotterdam, Department of Radiology & Nuclear Medicine, Erasmus MC, Rotterdam, Netherlands
| | - Pedro G Vaz
- Laboratory for Instrumentation, Biomedical Engineering and Radiation Physics (LIBPhys-UC), Department of Physics, University of Coimbra, Coimbra, Portugal
| | - Luisa Sánchez Brea
- Biomedical Imaging Group Rotterdam, Department of Radiology & Nuclear Medicine, Erasmus MC, Rotterdam, Netherlands
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23
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Automatic Segmentation of the Retinal Nerve Fiber Layer by Means of Mathematical Morphology and Deformable Models in 2D Optical Coherence Tomography Imaging. SENSORS 2021; 21:s21238027. [PMID: 34884031 PMCID: PMC8659929 DOI: 10.3390/s21238027] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 11/17/2021] [Accepted: 11/25/2021] [Indexed: 11/17/2022]
Abstract
Glaucoma is a neurodegenerative disease process that leads to progressive damage of the optic nerve to produce visual impairment and blindness. Spectral-domain OCT technology enables peripapillary circular scans of the retina and the measurement of the thickness of the retinal nerve fiber layer (RNFL) for the assessment of the disease status or progression in glaucoma patients. This paper describes a new approach to segment and measure the retinal nerve fiber layer in peripapillary OCT images. The proposed method consists of two stages. In the first one, morphological operators robustly detect the coarse location of the layer boundaries, despite the speckle noise and diverse artifacts in the OCT image. In the second stage, deformable models are initialized with the results of the previous stage to perform a fine segmentation of the boundaries, providing an accurate measurement of the entire RNFL. The results of the RNFL segmentation were qualitatively assessed by ophthalmologists, and the measurements of the thickness of the RNFL were quantitatively compared with those provided by the OCT inbuilt software as well as the state-of-the-art methods.
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