1
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Brown EE, Guy AA, Holroyd NA, Sweeney PW, Gourmet L, Coleman H, Walsh C, Markaki AE, Shipley R, Rajendram R, Walker-Samuel S. Physics-informed deep generative learning for quantitative assessment of the retina. Nat Commun 2024; 15:6859. [PMID: 39127778 PMCID: PMC11316734 DOI: 10.1038/s41467-024-50911-y] [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: 07/26/2023] [Accepted: 07/25/2024] [Indexed: 08/12/2024] Open
Abstract
Disruption of retinal vasculature is linked to various diseases, including diabetic retinopathy and macular degeneration, leading to vision loss. We present here a novel algorithmic approach that generates highly realistic digital models of human retinal blood vessels, based on established biophysical principles, including fully-connected arterial and venous trees with a single inlet and outlet. This approach, using physics-informed generative adversarial networks (PI-GAN), enables the segmentation and reconstruction of blood vessel networks with no human input and which out-performs human labelling. Segmentation of DRIVE and STARE retina photograph datasets provided near state-of-the-art vessel segmentation, with training on only a small (n = 100) simulated dataset. Our findings highlight the potential of PI-GAN for accurate retinal vasculature characterization, with implications for improving early disease detection, monitoring disease progression, and improving patient care.
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Affiliation(s)
- Emmeline E Brown
- Centre for Computational Medicine, University College London, London, UK
- Moorfields Eye Hospital, London, UK
| | - Andrew A Guy
- Centre for Computational Medicine, University College London, London, UK
- Department of Engineering, University of Cambridge, Cambridge, UK
| | - Natalie A Holroyd
- Centre for Computational Medicine, University College London, London, UK
| | - Paul W Sweeney
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, UK
| | - Lucie Gourmet
- Centre for Computational Medicine, University College London, London, UK
| | - Hannah Coleman
- Centre for Computational Medicine, University College London, London, UK
| | - Claire Walsh
- Centre for Computational Medicine, University College London, London, UK
- Department of Mechanical Engineering, University College London, London, UK
| | - Athina E Markaki
- Department of Engineering, University of Cambridge, Cambridge, UK
| | - Rebecca Shipley
- Centre for Computational Medicine, University College London, London, UK
- Department of Mechanical Engineering, University College London, London, UK
| | - Ranjan Rajendram
- Moorfields Eye Hospital, London, UK
- Institute of Ophthalmology, University College London, London, UK
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2
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Liu J, Zhao J, Xiao J, Zhao G, Xu P, Yang Y, Gong S. Unsupervised domain adaptation multi-level adversarial learning-based crossing-domain retinal vessel segmentation. Comput Biol Med 2024; 178:108759. [PMID: 38917530 DOI: 10.1016/j.compbiomed.2024.108759] [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: 12/15/2023] [Revised: 06/12/2024] [Accepted: 06/13/2024] [Indexed: 06/27/2024]
Abstract
BACKGROUND The retinal vasculature, a crucial component of the human body, mirrors various illnesses such as cardiovascular disease, glaucoma, and retinopathy. Accurate segmentation of retinal vessels in funduscopic images is essential for diagnosing and understanding these conditions. However, existing segmentation models often struggle with images from different sources, making accurate segmentation in crossing-source fundus images challenging. METHODS To address the crossing-source segmentation issues, this paper proposes a novel Multi-level Adversarial Learning and Pseudo-label Denoising-based Self-training Framework (MLAL&PDSF). Expanding on our previously proposed Multiscale Context Gating with Breakpoint and Spatial Dual Attention Network (MCG&BSA-Net), MLAL&PDSF introduces a multi-level adversarial network that operates at both the feature and image layers to align distributions between the target and source domains. Additionally, it employs a distance comparison technique to refine pseudo-labels generated during the self-training process. By comparing the distance between the pseudo-labels and the network predictions, the framework identifies and corrects inaccuracies, thus enhancing the accuracy of the fine vessel segmentation. RESULTS We have conducted extensive validation and comparative experiments on the CHASEDB1, STARE, and HRF datasets to evaluate the efficacy of the MLAL&PDSF. The evaluation metrics included the area under the operating characteristic curve (AUC), sensitivity (SE), specificity (SP), accuracy (ACC), and balanced F-score (F1). The performance results from unsupervised domain adaptive segmentation are remarkable: for DRIVE to CHASEDB1, results are AUC: 0.9806, SE: 0.7400, SP: 0.9737, ACC: 0.9874, and F1: 0.8851; for DRIVE to STARE, results are AUC: 0.9827, SE: 0.7944, SP: 0.9651, ACC: 0.9826, and F1: 0.8326. CONCLUSION These results demonstrate the effectiveness and robustness of MLAL&PDSF in achieving accurate segmentation results from crossing-domain retinal vessel datasets. The framework lays a solid foundation for further advancements in cross-domain segmentation and enhances the diagnosis and understanding of related diseases.
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Affiliation(s)
- Jinping Liu
- College of Information Science and Engineering, Hunan Normal University, Changsha, Hunan, 410081, China.
| | - Junqi Zhao
- College of Information Science and Engineering, Hunan Normal University, Changsha, Hunan, 410081, China.
| | - Jingri Xiao
- College of Information Science and Engineering, Hunan Normal University, Changsha, Hunan, 410081, China.
| | - Gangjin Zhao
- College of Information Science and Engineering, Hunan Normal University, Changsha, Hunan, 410081, China.
| | - Pengfei Xu
- College of Information Science and Engineering, Hunan Normal University, Changsha, Hunan, 410081, China.
| | - Yimei Yang
- School of Mathematics and Statistics, Hunan Normal University, Changsha, Hunan, 410081, China; College of Computer and Artificial Intelligence (Software College), Huaihua University, Huaihua, Hunan, 418000, China.
| | - Subo Gong
- Department of Geriatrics, The Second Xiangya Hospital of Central South University, Changsha, 410011, China.
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3
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Yagis E, Aslani S, Jain Y, Zhou Y, Rahmani S, Brunet J, Bellier A, Werlein C, Ackermann M, Jonigk D, Tafforeau P, Lee PD, Walsh C. Deep Learning for 3D Vascular Segmentation in Phase Contrast Tomography. RESEARCH SQUARE 2024:rs.3.rs-4613439. [PMID: 39070623 PMCID: PMC11276017 DOI: 10.21203/rs.3.rs-4613439/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
Automated blood vessel segmentation is critical for biomedical image analysis, as vessel morphology changes are associated with numerous pathologies. Still, precise segmentation is difficult due to the complexity of vascular structures, anatomical variations across patients, the scarcity of annotated public datasets, and the quality of images. Our goal is to provide a foundation on the topic and identify a robust baseline model for application to vascular segmentation using a new imaging modality, Hierarchical Phase-Contrast Tomography (HiP-CT). We begin with an extensive review of current machine learning approaches for vascular segmentation across various organs. Our work introduces a meticulously curated training dataset, verified by double annotators, consisting of vascular data from three kidneys imaged using Hierarchical Phase-Contrast Tomography (HiP-CT) as part of the Human Organ Atlas Project. HiP-CT, pioneered at the European Synchrotron Radiation Facility in 2020, revolutionizes 3D organ imaging by offering resolution around 20μm/voxel, and enabling highly detailed localized zooms up to 1μm/voxel without physical sectioning. We leverage the nnU-Net framework to evaluate model performance on this high-resolution dataset, using both known and novel samples, and implementing metrics tailored for vascular structures. Our comprehensive review and empirical analysis on HiP-CT data sets a new standard for evaluating machine learning models in high-resolution organ imaging. Our three experiments yielded Dice scores of 0.9523 and 0.9410, and 0.8585, respectively. Nevertheless, DSC primarily assesses voxel-to-voxel concordance, overlooking several crucial characteristics of the vessels and should not be the sole metric for deciding the performance of vascular segmentation. Our results show that while segmentations yielded reasonably high scores-such as centerline Dice values ranging from 0.82 to 0.88, certain errors persisted. Specifically, large vessels that collapsed due to the lack of hydro-static pressure (HiP-CT is an ex vivo technique) were segmented poorly. Moreover, decreased connectivity in finer vessels and higher segmentation errors at vessel boundaries were observed. Such errors, particularly in significant vessels, obstruct the understanding of the structures by interrupting vascular tree connectivity. Through our review and outputs, we aim to set a benchmark for subsequent model evaluations using various modalities, especially with the HiP-CT imaging database.
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Affiliation(s)
- Ekin Yagis
- Department of Mechanical Engineering, University College London, London, UK
| | - Shahab Aslani
- Department of Mechanical Engineering, University College London, London, UK
- Centre for Medical Image Computing, University College London, London UK
| | - Yashvardhan Jain
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, USA
| | - Yang Zhou
- Department of Mechanical Engineering, University College London, London, UK
| | - Shahrokh Rahmani
- Department of Mechanical Engineering, University College London, London, UK
| | - Joseph Brunet
- Department of Mechanical Engineering, University College London, London, UK
- European Synchrotron Radiation Facility, Grenoble, France
| | | | - Christopher Werlein
- Institute of Pathology, Hannover Medical School, Carl-Neuberg-Straße 1, 30625, Hannover, Germany
| | | | - Danny Jonigk
- Member of the German Center for Lung Research (DZL), Biomedical Research in Endstage and Obstructive Lung Disease Hannover (BREATH), Hannover, Germany
| | - Paul Tafforeau
- European Synchrotron Radiation Facility, Grenoble, France
| | - Peter D. Lee
- Department of Mechanical Engineering, University College London, London, UK
| | - Claire Walsh
- Department of Mechanical Engineering, University College London, London, UK
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4
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Waisberg E, Ong J, Kamran SA, Masalkhi M, Paladugu P, Zaman N, Lee AG, Tavakkoli A. Generative artificial intelligence in ophthalmology. Surv Ophthalmol 2024:S0039-6257(24)00044-4. [PMID: 38762072 DOI: 10.1016/j.survophthal.2024.04.009] [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: 12/23/2022] [Revised: 04/28/2024] [Accepted: 04/29/2024] [Indexed: 05/20/2024]
Abstract
Generative AI has revolutionized medicine over the past several years. A generative adversarial network (GAN) is a deep learning framework that has become a powerful technique in medicine, particularly in ophthalmology and image analysis. In this paper we review the current ophthalmic literature involving GANs, and highlight key contributions in the field. We briefly touch on ChatGPT, another application of generative AI, and its potential in ophthalmology. We also explore the potential uses for GANs in ocular imaging, with a specific emphasis on 3 primary domains: image enhancement, disease identification, and generating of synthetic data. PubMed, Ovid MEDLINE, Google Scholar were searched from inception to October 30, 2022 to identify applications of GAN in ophthalmology. A total of 40 papers were included in this review. We cover various applications of GANs in ophthalmic-related imaging including optical coherence tomography, orbital magnetic resonance imaging, fundus photography, and ultrasound; however, we also highlight several challenges, that resulted in the generation of inaccurate and atypical results during certain iterations. Finally, we examine future directions and considerations for generative AI in ophthalmology.
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Affiliation(s)
- Ethan Waisberg
- Department of Ophthalmology, University of Cambridge, Cambridge, United Kingdom.
| | - Joshua Ong
- Michigan Medicine, University of Michigan, Ann Arbor, United States
| | - Sharif Amit Kamran
- School of Medicine, University College Dublin, Belfield, Dublin, Ireland
| | - Mouayad Masalkhi
- School of Medicine, University College Dublin, Belfield, Dublin, Ireland
| | - Phani Paladugu
- Sidney Kimmel Medical College, Thomas Jefferson University, Philadelphia, Pennsylvania, United States; Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, United States
| | - Nasif Zaman
- Human-Machine Perception Laboratory, Department of Computer Science and Engineering, University of Nevada, Reno, Reno, Nevada, United States
| | - Andrew G Lee
- Center for Space Medicine, Baylor College of Medicine, Houston, Texas, United States; Department of Ophthalmology, Blanton Eye Institute, Houston Methodist Hospital, Houston, Texas, United States; The Houston Methodist Research Institute, Houston Methodist Hospital, Houston, Texas, United States; Departments of Ophthalmology, Neurology, and Neurosurgery, Weill Cornell Medicine, New York, New York, United States; Department of Ophthalmology, University of Texas Medical Branch, Galveston, Texas, United States; University of Texas MD Anderson Cancer Center, Houston, Texas, United States; Texas A&M College of Medicine, Texas, United States; Department of Ophthalmology, The University of Iowa Hospitals and Clinics, Iowa City, Iowa, United States
| | - Alireza Tavakkoli
- Human-Machine Perception Laboratory, Department of Computer Science and Engineering, University of Nevada, Reno, Reno, Nevada, United States
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5
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Chen Y, Bai Y, Zhang Y. Optic disc and cup segmentation for glaucoma detection using Attention U-Net incorporating residual mechanism. PeerJ Comput Sci 2024; 10:e1941. [PMID: 38660163 PMCID: PMC11042003 DOI: 10.7717/peerj-cs.1941] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2023] [Accepted: 02/26/2024] [Indexed: 04/26/2024]
Abstract
Glaucoma is a common eye disease that can cause blindness. Accurate detection of the optic disc and cup disc is crucial for glaucoma diagnosis. Algorithm models based on artificial intelligence can assist doctors in improving detection performance. In this article, U-Net is used as the backbone network, and the attention and residual modules are integrated to construct an end-to-end convolutional neural network model for optic disc and cup disc segmentation. The U-Net backbone is used to infer the basic position information of optic disc and cup disc, the attention module enhances the model's ability to represent and extract features of optic disc and cup disc, and the residual module alleviates gradient disappearance or explosion that may occur during feature representation of the neural network. The proposed model is trained and tested on the DRISHTI-GS1 dataset. Results show that compared with the original U-Net method, our model can more effectively separate optic disc and cup disc in terms of overlap error, sensitivity, and specificity.
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Affiliation(s)
- Yuanyuan Chen
- School of Information Technology, Luoyang Normal University, Luoyang, China
| | - Yongpeng Bai
- School of Information Technology, Luoyang Normal University, Luoyang, China
| | - Yifan Zhang
- School of Information Technology, Luoyang Normal University, Luoyang, China
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6
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Zheng C, Li H, Ge Y, He Y, Yi Y, Zhu M, Sun H, Kong J. Retinal vessel segmentation based on multi-scale feature and style transfer. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2024; 21:49-74. [PMID: 38303413 DOI: 10.3934/mbe.2024003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/03/2024]
Abstract
Retinal vessel segmentation is very important for diagnosing and treating certain eye diseases. Recently, many deep learning-based retinal vessel segmentation methods have been proposed; however, there are still many shortcomings (e.g., they cannot obtain satisfactory results when dealing with cross-domain data or segmenting small blood vessels). To alleviate these problems and avoid overly complex models, we propose a novel network based on a multi-scale feature and style transfer (MSFST-NET) for retinal vessel segmentation. Specifically, we first construct a lightweight segmentation module named MSF-Net, which introduces the selective kernel (SK) module to increase the multi-scale feature extraction ability of the model to achieve improved small blood vessel segmentation. Then, to alleviate the problem of model performance degradation when segmenting cross-domain datasets, we propose a style transfer module and a pseudo-label learning strategy. The style transfer module is used to reduce the style difference between the source domain image and the target domain image to improve the segmentation performance for the target domain image. The pseudo-label learning strategy is designed to be combined with the style transfer module to further boost the generalization ability of the model. Moreover, we trained and tested our proposed MSFST-NET in experiments on the DRIVE and CHASE_DB1 datasets. The experimental results demonstrate that MSFST-NET can effectively improve the generalization ability of the model on cross-domain datasets and achieve improved retinal vessel segmentation results than other state-of-the-art methods.
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Affiliation(s)
- Caixia Zheng
- Jilin Animation Institute, Changchun 130013, China
- College of Information Science and Technology, Northeast Normal University, Changchun 130117, China
| | - Huican Li
- College of Information Science and Technology, Northeast Normal University, Changchun 130117, China
| | - Yingying Ge
- Jilin Animation Institute, Changchun 130013, China
| | - Yanlin He
- College of Information Science and Technology, Northeast Normal University, Changchun 130117, China
| | - Yugen Yi
- School of Software, Jiangxi Normal University, Nanchang 330022, China
| | - Meili Zhu
- Jilin Animation Institute, Changchun 130013, China
| | - Hui Sun
- School of Science and Technology, Changchun Humanities and Sciences College, Changchun 130117, China
| | - Jun Kong
- College of Information Science and Technology, Northeast Normal University, Changchun 130117, China
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7
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Lin L, Peng L, He H, Cheng P, Wu J, Wong KKY, Tang X. YoloCurvSeg: You only label one noisy skeleton for vessel-style curvilinear structure segmentation. Med Image Anal 2023; 90:102937. [PMID: 37672901 DOI: 10.1016/j.media.2023.102937] [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: 01/21/2023] [Revised: 06/30/2023] [Accepted: 08/16/2023] [Indexed: 09/08/2023]
Abstract
Weakly-supervised learning (WSL) has been proposed to alleviate the conflict between data annotation cost and model performance through employing sparsely-grained (i.e., point-, box-, scribble-wise) supervision and has shown promising performance, particularly in the image segmentation field. However, it is still a very challenging task due to the limited supervision, especially when only a small number of labeled samples are available. Additionally, almost all existing WSL segmentation methods are designed for star-convex structures which are very different from curvilinear structures such as vessels and nerves. In this paper, we propose a novel sparsely annotated segmentation framework for curvilinear structures, named YoloCurvSeg. A very essential component of YoloCurvSeg is image synthesis. Specifically, a background generator delivers image backgrounds that closely match the real distributions through inpainting dilated skeletons. The extracted backgrounds are then combined with randomly emulated curves generated by a Space Colonization Algorithm-based foreground generator and through a multilayer patch-wise contrastive learning synthesizer. In this way, a synthetic dataset with both images and curve segmentation labels is obtained, at the cost of only one or a few noisy skeleton annotations. Finally, a segmenter is trained with the generated dataset and possibly an unlabeled dataset. The proposed YoloCurvSeg is evaluated on four publicly available datasets (OCTA500, CORN, DRIVE and CHASEDB1) and the results show that YoloCurvSeg outperforms state-of-the-art WSL segmentation methods by large margins. With only one noisy skeleton annotation (respectively 0.14%, 0.03%, 1.40%, and 0.65% of the full annotation), YoloCurvSeg achieves more than 97% of the fully-supervised performance on each dataset. Code and datasets will be released at https://github.com/llmir/YoloCurvSeg.
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Affiliation(s)
- Li Lin
- Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, China; Department of Electrical and Electronic Engineering, the University of Hong Kong, Hong Kong, China; Jiaxing Research Institute, Southern University of Science and Technology, Jiaxing, China
| | - Linkai Peng
- Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Huaqing He
- Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, China; Jiaxing Research Institute, Southern University of Science and Technology, Jiaxing, China
| | - Pujin Cheng
- Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, China; Jiaxing Research Institute, Southern University of Science and Technology, Jiaxing, China
| | - Jiewei Wu
- Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Kenneth K Y Wong
- Department of Electrical and Electronic Engineering, the University of Hong Kong, Hong Kong, China
| | - Xiaoying Tang
- Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, China; Jiaxing Research Institute, Southern University of Science and Technology, Jiaxing, China.
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8
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Chen X, Peng Y, Li D, Sun J. DMCA-GAN: Dual Multilevel Constrained Attention GAN for MRI-Based Hippocampus Segmentation. J Digit Imaging 2023; 36:2532-2553. [PMID: 37735310 PMCID: PMC10584805 DOI: 10.1007/s10278-023-00854-5] [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: 09/26/2022] [Revised: 04/29/2023] [Accepted: 05/17/2023] [Indexed: 09/23/2023] Open
Abstract
Precise segmentation of the hippocampus is essential for various human brain activity and neurological disorder studies. To overcome the small size of the hippocampus and the low contrast of MR images, a dual multilevel constrained attention GAN for MRI-based hippocampus segmentation is proposed in this paper, which is used to provide a relatively effective balance between suppressing noise interference and enhancing feature learning. First, we design the dual-GAN backbone to effectively compensate for the spatial information damage caused by multiple pooling operations in the feature generation stage. Specifically, dual-GAN performs joint adversarial learning on the multiscale feature maps at the end of the generator, which yields an average Dice coefficient (DSC) gain of 5.95% over the baseline. Next, to suppress MRI high-frequency noise interference, a multilayer information constraint unit is introduced before feature decoding, which improves the sensitivity of the decoder to forecast features by 5.39% and effectively alleviates the network overfitting problem. Then, to refine the boundary segmentation effects, we construct a multiscale feature attention restraint mechanism, which forces the network to concentrate more on effective multiscale details, thus improving the robustness. Furthermore, the dual discriminators D1 and D2 also effectively prevent the negative migration phenomenon. The proposed DMCA-GAN obtained a DSC of 90.53% on the Medical Segmentation Decathlon (MSD) dataset with tenfold cross-validation, which is superior to the backbone by 3.78%.
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Affiliation(s)
- Xue Chen
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, 266590, Shandong, China
| | - Yanjun Peng
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, 266590, Shandong, China.
- Shandong Province Key Laboratory of Wisdom Mining Information Technology, Shandong University of Science and Technology, Qingdao, 266590, Shandong, China.
| | - Dapeng Li
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, 266590, Shandong, China
| | - Jindong Sun
- College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao, 266590, Shandong, China
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9
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Guo X, Li R, Lu X, Zhang X, Wu Q, Tian Q, Guo B, Tang G, Xu J, Feng J, Zhao L, Ling S, Dong Z, Song J, Bi H. Quantization of Optic Disc Characteristics in Young Adults Based on Artificial Intelligence. Curr Eye Res 2023; 48:1068-1077. [PMID: 37555317 DOI: 10.1080/02713683.2023.2244700] [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: 02/21/2023] [Revised: 07/21/2023] [Accepted: 07/28/2023] [Indexed: 08/10/2023]
Abstract
PURPOSE This study aimed to automatically and quantitatively analyse the characteristics of the optic disc by applying artificial intelligence (AI) to fundus images. METHODS A total of 1084 undergraduates were recruited in this cross-sectional study. The optic disc area, cup-to-disc ratio (C/D), optic disc tilt, and the area, width, and height of peripapillary atrophy (PPA) were automatically and quantitatively detected using AI. Based on axial length (AL), participants were divided into five groups: Group 1 (AL ≤ 23 mm); Group 2 (23 mm < AL≤ 24 mm); Group 3 (24 mm < AL≤ 25 mm); Group 4 (25 mm < AL< 26 mm) and Group 5 (AL ≥ 26 mm). Relationships between ocular parameters and optic disc characteristics were analysed. RESULT A total of 999 undergraduates were included in the analysis. The prevalence of optic disc tilting and PPA were 47.1% and 92.5%, respectively, and increased with the severity of myopia. The mean optic disc area, PPA area, C/D, and optic disc tilt ratio were 1.97 ± 0.46 mm2, 0.84 ± 0.59 mm2, 0.18 ± 0.07, and 0.81 ± 0.08, respectively. In Group 5, the average optic disc area (1.84 ± 0.41 mm2) and optic disc tilt ratio (0.79 ± 0.08) were significantly smaller and the PPA area (1.12 ± 0.61 mm2) was significantly larger than those in the other groups. AL was negatively correlated with optic disc area and optic disc tilt ratio (r=-0.271, -0.219; both p < 0.001) and positively correlated with PPA area, width, and height (r = 0.421, 0.426, 0.345; all p < 0.01). A greater AL (β = 0.284, p < 0.01) and a smaller optic disc tilt ratio (β=-0.516, p < 0.01) were related to a larger PPA area. CONCLUSION The characteristics of the optic disc can be feasibly and efficiently extracted using AI. The quantization of the optic disc might provide new indicators for clinicians to evaluate the degree of myopia.
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Affiliation(s)
- Xiaoxiao Guo
- Shandong University of Traditional Chinese Medicine, Jinan, P. R. China
| | - Runkuan Li
- Shandong University of Traditional Chinese Medicine, Jinan, P. R. China
| | - Xiuzhen Lu
- Affiliated Eye Hospital of Shandong, University of Traditional Chinese Medicine, Jinan, P. R. China
- Shandong Provincial Key Laboratory of Integrated Traditional Chinese and Western Medicine for Prevention and Therapy of Ocular Diseases, Key Laboratory of Integrated Traditional Chinese and Western Medicine for Prevention and Therapy of Ocular Diseases, Universities of Shandong; Eye Institute of Shandong University of Traditional Chinese Medicine, Jinan, P. R. China
| | - Xiuyan Zhang
- Affiliated Eye Hospital of Shandong, University of Traditional Chinese Medicine, Jinan, P. R. China
- Shandong Provincial Key Laboratory of Integrated Traditional Chinese and Western Medicine for Prevention and Therapy of Ocular Diseases, Key Laboratory of Integrated Traditional Chinese and Western Medicine for Prevention and Therapy of Ocular Diseases, Universities of Shandong; Eye Institute of Shandong University of Traditional Chinese Medicine, Jinan, P. R. China
| | - Qiuxin Wu
- Affiliated Eye Hospital of Shandong, University of Traditional Chinese Medicine, Jinan, P. R. China
- Shandong Provincial Key Laboratory of Integrated Traditional Chinese and Western Medicine for Prevention and Therapy of Ocular Diseases, Key Laboratory of Integrated Traditional Chinese and Western Medicine for Prevention and Therapy of Ocular Diseases, Universities of Shandong; Eye Institute of Shandong University of Traditional Chinese Medicine, Jinan, P. R. China
| | - Qingmei Tian
- Affiliated Eye Hospital of Shandong, University of Traditional Chinese Medicine, Jinan, P. R. China
- Shandong Provincial Key Laboratory of Integrated Traditional Chinese and Western Medicine for Prevention and Therapy of Ocular Diseases, Key Laboratory of Integrated Traditional Chinese and Western Medicine for Prevention and Therapy of Ocular Diseases, Universities of Shandong; Eye Institute of Shandong University of Traditional Chinese Medicine, Jinan, P. R. China
| | - Bin Guo
- Shandong University of Traditional Chinese Medicine, Jinan, P. R. China
- Shandong Provincial Key Laboratory of Integrated Traditional Chinese and Western Medicine for Prevention and Therapy of Ocular Diseases, Key Laboratory of Integrated Traditional Chinese and Western Medicine for Prevention and Therapy of Ocular Diseases, Universities of Shandong; Eye Institute of Shandong University of Traditional Chinese Medicine, Jinan, P. R. China
| | - Guodong Tang
- Affiliated Eye Hospital of Shandong, University of Traditional Chinese Medicine, Jinan, P. R. China
- Shandong Provincial Key Laboratory of Integrated Traditional Chinese and Western Medicine for Prevention and Therapy of Ocular Diseases, Key Laboratory of Integrated Traditional Chinese and Western Medicine for Prevention and Therapy of Ocular Diseases, Universities of Shandong; Eye Institute of Shandong University of Traditional Chinese Medicine, Jinan, P. R. China
| | - Jing Xu
- Affiliated Eye Hospital of Shandong, University of Traditional Chinese Medicine, Jinan, P. R. China
- Shandong Provincial Key Laboratory of Integrated Traditional Chinese and Western Medicine for Prevention and Therapy of Ocular Diseases, Key Laboratory of Integrated Traditional Chinese and Western Medicine for Prevention and Therapy of Ocular Diseases, Universities of Shandong; Eye Institute of Shandong University of Traditional Chinese Medicine, Jinan, P. R. China
| | - Jiaojiao Feng
- Shandong University of Traditional Chinese Medicine, Jinan, P. R. China
| | - Lili Zhao
- Shandong University of Traditional Chinese Medicine, Jinan, P. R. China
| | - Saiguang Ling
- EVision Technology (Beijing) Co., Ltd, Beijing, P. R. China
| | - Zhou Dong
- EVision Technology (Beijing) Co., Ltd, Beijing, P. R. China
| | - Jike Song
- Shandong University of Traditional Chinese Medicine, Jinan, P. R. China
- Shandong Provincial Key Laboratory of Integrated Traditional Chinese and Western Medicine for Prevention and Therapy of Ocular Diseases, Key Laboratory of Integrated Traditional Chinese and Western Medicine for Prevention and Therapy of Ocular Diseases, Universities of Shandong; Eye Institute of Shandong University of Traditional Chinese Medicine, Jinan, P. R. China
| | - Hongsheng Bi
- Affiliated Eye Hospital of Shandong, University of Traditional Chinese Medicine, Jinan, P. R. China
- Shandong Provincial Key Laboratory of Integrated Traditional Chinese and Western Medicine for Prevention and Therapy of Ocular Diseases, Key Laboratory of Integrated Traditional Chinese and Western Medicine for Prevention and Therapy of Ocular Diseases, Universities of Shandong; Eye Institute of Shandong University of Traditional Chinese Medicine, Jinan, P. R. China
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10
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Rashidisabet H, Sethi A, Jindarak P, Edmonds J, Chan RVP, Leiderman YI, Vajaranant TS, Yi D. Validating the Generalizability of Ophthalmic Artificial Intelligence Models on Real-World Clinical Data. Transl Vis Sci Technol 2023; 12:8. [PMID: 37922149 PMCID: PMC10629532 DOI: 10.1167/tvst.12.11.8] [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: 10/09/2022] [Accepted: 08/21/2023] [Indexed: 11/05/2023] Open
Abstract
Purpose This study aims to investigate generalizability of deep learning (DL) models trained on commonly used public fundus images to an instance of real-world data (RWD) for glaucoma diagnosis. Methods We used Illinois Eye and Ear Infirmary fundus data set as an instance of RWD in addition to six publicly available fundus data sets. We compared the performance of DL-trained models on public data and RWD for glaucoma classification and optic disc (OD) segmentation tasks. For each task, we created models trained on each data set, respectively, and each model was tested on both data sets. We further examined each model's decision-making process and learned embeddings for the glaucoma classification task. Results Using public data for the test set, public-trained models outperformed RWD-trained models in OD segmentation and glaucoma classification with a mean intersection over union of 96.3% and mean area under the receiver operating characteristic curve of 95.0%, respectively. Using the RWD test set, the performance of public models decreased by 8.0% and 18.4% to 85.6% and 76.6% for OD segmentation and glaucoma classification tasks, respectively. RWD models outperformed public models on RWD test sets by 2.0% and 9.5%, respectively, in OD segmentation and glaucoma classification tasks. Conclusions DL models trained on commonly used public data have limited ability to generalize to RWD for classifying glaucoma. They perform similarly to RWD models for OD segmentation. Translational Relevance RWD is a potential solution for improving generalizability of DL models and enabling clinical translations in the care of prevalent blinding ophthalmic conditions, such as glaucoma.
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Affiliation(s)
- Homa Rashidisabet
- Department of Biomedical Engineering, University of Illinois Chicago, Chicago, IL, USA
- Artificial Intelligence in Ophthalmology (Ai-O) Center, University of Illinois Chicago, Chicago, IL, USA
| | - Abhishek Sethi
- Artificial Intelligence in Ophthalmology (Ai-O) Center, University of Illinois Chicago, Chicago, IL, USA
- Illinois Eye and Ear Infirmary, Department of Ophthalmology and Visual Sciences, University of Illinois Chicago, Chicago, IL, USA
| | - Ponpawee Jindarak
- Illinois Eye and Ear Infirmary, Department of Ophthalmology and Visual Sciences, University of Illinois Chicago, Chicago, IL, USA
| | - James Edmonds
- Artificial Intelligence in Ophthalmology (Ai-O) Center, University of Illinois Chicago, Chicago, IL, USA
- Illinois Eye and Ear Infirmary, Department of Ophthalmology and Visual Sciences, University of Illinois Chicago, Chicago, IL, USA
| | - R V Paul Chan
- Artificial Intelligence in Ophthalmology (Ai-O) Center, University of Illinois Chicago, Chicago, IL, USA
- Illinois Eye and Ear Infirmary, Department of Ophthalmology and Visual Sciences, University of Illinois Chicago, Chicago, IL, USA
| | - Yannek I Leiderman
- Department of Biomedical Engineering, University of Illinois Chicago, Chicago, IL, USA
- Artificial Intelligence in Ophthalmology (Ai-O) Center, University of Illinois Chicago, Chicago, IL, USA
- Illinois Eye and Ear Infirmary, Department of Ophthalmology and Visual Sciences, University of Illinois Chicago, Chicago, IL, USA
| | - Thasarat Sutabutr Vajaranant
- Artificial Intelligence in Ophthalmology (Ai-O) Center, University of Illinois Chicago, Chicago, IL, USA
- Illinois Eye and Ear Infirmary, Department of Ophthalmology and Visual Sciences, University of Illinois Chicago, Chicago, IL, USA
| | - Darvin Yi
- Department of Biomedical Engineering, University of Illinois Chicago, Chicago, IL, USA
- Artificial Intelligence in Ophthalmology (Ai-O) Center, University of Illinois Chicago, Chicago, IL, USA
- Illinois Eye and Ear Infirmary, Department of Ophthalmology and Visual Sciences, University of Illinois Chicago, Chicago, IL, USA
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11
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van der Schot A, Sikkel E, Niekolaas M, Spaanderman M, de Jong G. Placental Vessel Segmentation Using Pix2pix Compared to U-Net. J Imaging 2023; 9:226. [PMID: 37888333 PMCID: PMC10607321 DOI: 10.3390/jimaging9100226] [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: 07/24/2023] [Revised: 09/21/2023] [Accepted: 10/02/2023] [Indexed: 10/28/2023] Open
Abstract
Computer-assisted technologies have made significant progress in fetoscopic laser surgery, including placental vessel segmentation. However, the intra- and inter-procedure variabilities in the state-of-the-art segmentation methods remain a significant hurdle. To address this, we investigated the use of conditional generative adversarial networks (cGANs) for fetoscopic image segmentation and compared their performance with the benchmark U-Net technique for placental vessel segmentation. Two deep-learning models, U-Net and pix2pix (a popular cGAN model), were trained and evaluated using a publicly available dataset and an internal validation set. The overall results showed that the pix2pix model outperformed the U-Net model, with a Dice score of 0.80 [0.70; 0.86] versus 0.75 [0.0.60; 0.84] (p-value < 0.01) and an Intersection over Union (IoU) score of 0.70 [0.61; 0.77] compared to 0.66 [0.53; 0.75] (p-value < 0.01), respectively. The internal validation dataset further validated the superiority of the pix2pix model, achieving Dice and IoU scores of 0.68 [0.53; 0.79] and 0.59 [0.49; 0.69] (p-value < 0.01), respectively, while the U-Net model obtained scores of 0.53 [0.49; 0.64] and 0.49 [0.17; 0.56], respectively. This study successfully compared U-Net and pix2pix models for placental vessel segmentation in fetoscopic images, demonstrating improved results with the cGAN-based approach. However, the challenge of achieving generalizability still needs to be addressed.
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Affiliation(s)
- Anouk van der Schot
- Obstetrics & Gynecology, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
| | - Esther Sikkel
- Obstetrics & Gynecology, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
| | - Marèll Niekolaas
- Obstetrics & Gynecology, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
| | - Marc Spaanderman
- Obstetrics & Gynecology, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
- Obstetrics & Gynecology, Maastricht University Medical Center, 6229 ER Maastricht, The Netherlands
- Department of GROW, School for Oncology and Reproduction, Maastricht University, 6229 ER Maastricht, The Netherlands
| | - Guido de Jong
- 3D Lab, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands
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12
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Shao HC, Chen CY, Chang MH, Yu CH, Lin CW, Yang JW. Retina-TransNet: A Gradient-Guided Few-Shot Retinal Vessel Segmentation Net. IEEE J Biomed Health Inform 2023; 27:4902-4913. [PMID: 37490372 DOI: 10.1109/jbhi.2023.3298710] [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: 07/27/2023]
Abstract
Due to the high labor cost of physicians, it is difficult to collect a rich amount of manually-labeled medical images for developing learning-based computer-aided diagnosis (CADx) systems or segmentation algorithms. To tackle this issue, we reshape the image segmentation task as an image-to-image (I2I) translation problem and propose a retinal vascular segmentation network, which can achieve good cross-domain generalizability even with a small amount of training data. We devise primarily two components to facilitate this I2I-based segmentation method. The first is the constraints provided by the proposed gradient-vector-flow (GVF) loss, and, the second is a two-stage Unet (2Unet) generator with a skip connection. This configuration makes 2Unet's first-stage play a role similar to conventional Unet, but forces 2Unet's second stage to learn to be a refinement module. Extensive experiments show that by re-casting retinal vessel segmentation as an image-to-image translation problem, our I2I translator-based segmentation subnetwork achieves better cross-domain generalizability than existing segmentation methods. Our model, trained on one dataset, e.g., DRIVE, can produce segmentation results stably on datasets of other domains, e.g., CHASE-DB1, STARE, HRF, and DIARETDB1, even in low-shot circumstances.
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13
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Li Z, Zhao C, Han Z, Hong C. TUNet and domain adaptation based learning for joint optic disc and cup segmentation. Comput Biol Med 2023; 163:107209. [PMID: 37442009 DOI: 10.1016/j.compbiomed.2023.107209] [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: 02/11/2023] [Revised: 06/02/2023] [Accepted: 06/25/2023] [Indexed: 07/15/2023]
Abstract
Glaucoma is a chronic disorder that harms the optic nerves and causes irreversible blindness. The calculation of optic cup (OC) to optic disc (OD) ratio plays an important role in the primary screening and diagnosis of glaucoma. Thus, automatic and precise segmentations of OD and OC is highly preferable. Recently, deep neural networks demonstrate remarkable progress in the OD and OC segmentation, however, they are severely hindered in generalizing across different scanners and image resolution. In this work, we propose a novel domain adaptation-based framework to mitigate the performance degradation in OD and OC segmentation. We first devise an effective transformer-based segmentation network as a backbone to accurately segment the OD and OC regions. Then, to address the issue of domain shift, we introduce domain adaptation into the learning paradigm to encourage domain-invariant features. Since the segmentation-based domain adaptation loss is insufficient for capturing segmentation details, we further propose an auxiliary classifier to enable the discrimination on segmentation details. Exhaustive experiments on three public retinal fundus image datasets, i.e., REFUGE, Drishti-GS and RIM-ONE-r3, demonstrate our superior performance on the segmentation of OD and OC. These results suggest that our proposal has great potential to be an important component for an automated glaucoma screening system.
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Affiliation(s)
- Zhuorong Li
- Hangzhou City University, Hangzhou, 310015, Zhejiang, China.
| | - Chen Zhao
- Zhejiang University, Hangzhou, 310027, Zhejiang, China
| | - Zhike Han
- Hangzhou City University, Hangzhou, 310015, Zhejiang, China.
| | - Chaoyang Hong
- Zhejiang Provincial People's Hospital, Hangzhou, 310014, Zhejiang, China
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14
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Khan TM, Naqvi SS, Robles-Kelly A, Razzak I. Retinal vessel segmentation via a Multi-resolution Contextual Network and adversarial learning. Neural Netw 2023; 165:310-320. [PMID: 37327578 DOI: 10.1016/j.neunet.2023.05.029] [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: 02/27/2022] [Revised: 04/24/2023] [Accepted: 05/17/2023] [Indexed: 06/18/2023]
Abstract
Timely and affordable computer-aided diagnosis of retinal diseases is pivotal in precluding blindness. Accurate retinal vessel segmentation plays an important role in disease progression and diagnosis of such vision-threatening diseases. To this end, we propose a Multi-resolution Contextual Network (MRC-Net) that addresses these issues by extracting multi-scale features to learn contextual dependencies between semantically different features and using bi-directional recurrent learning to model former-latter and latter-former dependencies. Another key idea is training in adversarial settings for foreground segmentation improvement through optimization of the region-based scores. This novel strategy boosts the performance of the segmentation network in terms of the Dice score (and correspondingly Jaccard index) while keeping the number of trainable parameters comparatively low. We have evaluated our method on three benchmark datasets, including DRIVE, STARE, and CHASE, demonstrating its superior performance as compared with competitive approaches elsewhere in the literature.
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Affiliation(s)
- Tariq M Khan
- School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia.
| | - Syed S Naqvi
- Department of Electrical and Computer Engineering, COMSATS University Islamabad, Pakistan
| | - Antonio Robles-Kelly
- School of Information Technology, Faculty of Science Engineering & Built Environment, Deakin University, Locked Bag 20000, Geelong, Australia; Defence Science and Technology Group, 5111, Edinburgh, SA, Australia
| | - Imran Razzak
- School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia
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15
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Bhattacharya R, Hussain R, Chatterjee A, Paul D, Chatterjee S, Dey D. PY-Net: Rethinking segmentation frameworks with dense pyramidal operations for optic disc and cup segmentation from retinal fundus images. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104895] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/30/2023]
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16
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Aurangzeb K. A residual connection enabled deep neural network model for optic disk and optic cup segmentation for glaucoma diagnosis. Sci Prog 2023; 106:368504231201329. [PMID: 37743660 PMCID: PMC10521305 DOI: 10.1177/00368504231201329] [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] [Indexed: 09/26/2023]
Abstract
Glaucoma diagnosis at an early stage is vital for the timely initiation of its treatment for and preventing possible vision loss. For glaucoma diagnosis, an accurate estimation of the cup-to-disk ratio (CDR) is required. The current automatic CDR computation techniques attribute lower accuracy and higher complexity, which are important considerations for diagnostics system design to be used for such critical diagnoses. The current methods involve a deeper deep learning model, comprising a large number of parameters, which results in higher system complexity and training/testing time. To address these challenges, this paper proposes a Residual Connection (non-identity)-based Deep Neural Network (RC-DNN), which is based on non-identity residual connectivity for joint optic disk (OD) and optic cup (OC) detection. The proposed model is emboldened by efficient residual connectivity, which is beneficial in several ways. First, the model is efficient and can perform simultaneous segmentation of the OC and OD. Second, the efficient residual information flow permeates the vanishing gradient problem which results in faster converges of the model. Third, feature inspiration empowers the network to perform the segmentation with only a few network layers. We performed a comprehensive performance evaluation of the developed model based on its training in RIM-ONE and DRISHTIGS databases. For OC segmentation, for the images (test set) from {DRISHTI-GS and RIM-ONE} datasets, our proposed model achieves the dice coefficient, Jaccard coefficient, sensitivity, specificity, and balanced accuracy of {92.62, 86.52}, {86.87, 77.54}, {94.21, 95.36}, {99.83, 99.639}, and {94.2, 98.9}, respectively. These experimental results indicate that the developed model provides significant performance enhancement for joint OC and OD segmentation. Additionally, the reduced computational complexity based on reduced model parameters and higher segmentation accuracy provides the additional features of efficacy, robustness, and reliability of the developed model. These attributes of the developed model advocate for its deployment of population-scale glaucoma screening programs.
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Affiliation(s)
- Khursheed Aurangzeb
- Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
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17
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Son J, Shin JY, Kong ST, Park J, Kwon G, Kim HD, Park KH, Jung KH, Park SJ. An interpretable and interactive deep learning algorithm for a clinically applicable retinal fundus diagnosis system by modelling finding-disease relationship. Sci Rep 2023; 13:5934. [PMID: 37045856 PMCID: PMC10097752 DOI: 10.1038/s41598-023-32518-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2023] [Accepted: 03/28/2023] [Indexed: 04/14/2023] Open
Abstract
The identification of abnormal findings manifested in retinal fundus images and diagnosis of ophthalmic diseases are essential to the management of potentially vision-threatening eye conditions. Recently, deep learning-based computer-aided diagnosis systems (CADs) have demonstrated their potential to reduce reading time and discrepancy amongst readers. However, the obscure reasoning of deep neural networks (DNNs) has been the leading cause to reluctance in its clinical use as CAD systems. Here, we present a novel architectural and algorithmic design of DNNs to comprehensively identify 15 abnormal retinal findings and diagnose 8 major ophthalmic diseases from macula-centered fundus images with the accuracy comparable to experts. We then define a notion of counterfactual attribution ratio (CAR) which luminates the system's diagnostic reasoning, representing how each abnormal finding contributed to its diagnostic prediction. By using CAR, we show that both quantitative and qualitative interpretation and interactive adjustment of the CAD result can be achieved. A comparison of the model's CAR with experts' finding-disease diagnosis correlation confirms that the proposed model identifies the relationship between findings and diseases similarly as ophthalmologists do.
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Affiliation(s)
| | - Joo Young Shin
- Department of Ophthalmology, Seoul Metropolitan Government Seoul National University Boramae Medical Center, Seoul, Republic of Korea
| | | | | | | | - Hoon Dong Kim
- Department of Ophthalmology, College of Medicine, Soonchunhyang University, Cheonan, Republic of Korea
| | - Kyu Hyung Park
- Department of Ophthalmology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, 82, Gumi-ro 173 Beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do, 13620, Republic of Korea
| | - Kyu-Hwan Jung
- Department of Medical Device Research and Management, Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, 81 Irwon-ro, Gangnam-gu, Seoul, Republic of Korea.
| | - Sang Jun Park
- Department of Ophthalmology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, 82, Gumi-ro 173 Beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do, 13620, Republic of Korea.
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18
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GLAN: GAN Assisted Lightweight Attention Network for Biomedical Imaging Based Diagnostics. Cognit Comput 2023. [DOI: 10.1007/s12559-023-10131-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/09/2023]
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19
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An empirical study of preprocessing techniques with convolutional neural networks for accurate detection of chronic ocular diseases using fundus images. APPL INTELL 2023; 53:1548-1566. [PMID: 35528131 PMCID: PMC9059700 DOI: 10.1007/s10489-022-03490-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/08/2022] [Indexed: 01/07/2023]
Abstract
Chronic Ocular Diseases (COD) such as myopia, diabetic retinopathy, age-related macular degeneration, glaucoma, and cataract can affect the eye and may even lead to severe vision impairment or blindness. According to a recent World Health Organization (WHO) report on vision, at least 2.2 billion individuals worldwide suffer from vision impairment. Often, overt signs indicative of COD do not manifest until the disease has progressed to an advanced stage. However, if COD is detected early, vision impairment can be avoided by early intervention and cost-effective treatment. Ophthalmologists are trained to detect COD by examining certain minute changes in the retina, such as microaneurysms, macular edema, hemorrhages, and alterations in the blood vessels. The range of eye conditions is diverse, and each of these conditions requires a unique patient-specific treatment. Convolutional neural networks (CNNs) have demonstrated significant potential in multi-disciplinary fields, including the detection of a variety of eye diseases. In this study, we combined several preprocessing approaches with convolutional neural networks to accurately detect COD in eye fundus images. To the best of our knowledge, this is the first work that provides a qualitative analysis of preprocessing approaches for COD classification using CNN models. Experimental results demonstrate that CNNs trained on the region of interest segmented images outperform the models trained on the original input images by a substantial margin. Additionally, an ensemble of three preprocessing techniques outperformed other state-of-the-art approaches by 30% and 3%, in terms of Kappa and F 1 scores, respectively. The developed prototype has been extensively tested and can be evaluated on more comprehensive COD datasets for deployment in the clinical setup.
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20
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Alfonso-Francia G, Pedraza-Ortega JC, Badillo-Fernández M, Toledano-Ayala M, Aceves-Fernandez MA, Rodriguez-Resendiz J, Ko SB, Tovar-Arriaga S. Performance Evaluation of Different Object Detection Models for the Segmentation of Optical Cups and Discs. Diagnostics (Basel) 2022; 12:diagnostics12123031. [PMID: 36553037 PMCID: PMC9777130 DOI: 10.3390/diagnostics12123031] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 11/22/2022] [Accepted: 11/25/2022] [Indexed: 12/12/2022] Open
Abstract
Glaucoma is an eye disease that gradually deteriorates vision. Much research focuses on extracting information from the optic disc and optic cup, the structure used for measuring the cup-to-disc ratio. These structures are commonly segmented with deeplearning techniques, primarily using Encoder-Decoder models, which are hard to train and time-consuming. Object detection models using convolutional neural networks can extract features from fundus retinal images with good precision. However, the superiority of one model over another for a specific task is still being determined. The main goal of our approach is to compare object detection model performance to automate segment cups and discs on fundus images. This study brings the novelty of seeing the behavior of different object detection models in the detection and segmentation of the disc and the optical cup (Mask R-CNN, MS R-CNN, CARAFE, Cascade Mask R-CNN, GCNet, SOLO, Point_Rend), evaluated on Retinal Fundus Images for Glaucoma Analysis (REFUGE), and G1020 datasets. Reported metrics were Average Precision (AP), F1-score, IoU, and AUCPR. Several models achieved the highest AP with a perfect 1.000 when the threshold for IoU was set up at 0.50 on REFUGE, and the lowest was Cascade Mask R-CNN with an AP of 0.997. On the G1020 dataset, the best model was Point_Rend with an AP of 0.956, and the worst was SOLO with 0.906. It was concluded that the methods reviewed achieved excellent performance with high precision and recall values, showing efficiency and effectiveness. The problem of how many images are needed was addressed with an initial value of 100, with excellent results. Data augmentation, multi-scale handling, and anchor box size brought improvements. The capability to translate knowledge from one database to another shows promising results too.
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Affiliation(s)
- Gendry Alfonso-Francia
- Faculty of Engineering, Autonomous University of Querétaro, Santiago de Querétaro 76010, Mexico
- Department of Electrical and Computer Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, SK S7N 5A9, Canada
| | | | - Mariana Badillo-Fernández
- Instituto Mexicano de Oftalmología (IMO) I.A.P., Circuito Exterior Estadio Corregidora sn, Centro Sur, Santiago de Querétaro 76010, Mexico
| | - Manuel Toledano-Ayala
- Faculty of Engineering, Autonomous University of Querétaro, Santiago de Querétaro 76010, Mexico
| | | | | | - Seok-Bum Ko
- Department of Electrical and Computer Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, SK S7N 5A9, Canada
| | - Saul Tovar-Arriaga
- Faculty of Engineering, Autonomous University of Querétaro, Santiago de Querétaro 76010, Mexico
- Correspondence:
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21
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Haider A, Arsalan M, Park C, Sultan H, Park KR. Exploring deep feature-blending capabilities to assist glaucoma screening. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109918] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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22
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Improving vessel connectivity in retinal vessel segmentation via adversarial learning. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.110243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
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23
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Chen Y, Jin D, Guo B, Bai X. Attention-Assisted Adversarial Model for Cerebrovascular Segmentation in 3D TOF-MRA Volumes. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:3520-3532. [PMID: 35759584 DOI: 10.1109/tmi.2022.3186731] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Cerebrovascular segmentation in time-of-flight magnetic resonance angiography (TOF-MRA) volumes is essential for a variety of diagnostic and analytical applications. However, accurate cerebrovascular segmentation in 3D TOF-MRA is faced with multiple issues, including vast variations in cerebrovascular morphology and intensity, noisy background, and severe class imbalance between foreground cerebral vessels and background. In this work, a 3D adversarial network model called A-SegAN is proposed to segment cerebral vessels in TOF-MRA volumes. The proposed model is composed of a segmentation network A-SegS to predict segmentation maps, and a critic network A-SegC to discriminate predictions from ground truth. Based on this model, the aforementioned issues are addressed by the prevailing visual attention mechanism. First, A-SegS is incorporated with feature-attention blocks to filter out discriminative feature maps, though the cerebrovascular has varied appearances. Second, a hard-example-attention loss is exploited to boost the training of A-SegS on hard samples. Further, A-SegC is combined with an input-attention layer to attach importance to foreground cerebrovascular class. The proposed methods were evaluated on a self-constructed voxel-wise annotated cerebrovascular TOF-MRA segmentation dataset, and experimental results indicate that A-SegAN achieves competitive or better cerebrovascular segmentation results compared to other deep learning methods, effectively alleviating the above issues.
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24
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Iqbal S, Khan TM, Naveed K, Naqvi SS, Nawaz SJ. Recent trends and advances in fundus image analysis: A review. Comput Biol Med 2022; 151:106277. [PMID: 36370579 DOI: 10.1016/j.compbiomed.2022.106277] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2022] [Revised: 10/19/2022] [Accepted: 10/30/2022] [Indexed: 11/05/2022]
Abstract
Automated retinal image analysis holds prime significance in the accurate diagnosis of various critical eye diseases that include diabetic retinopathy (DR), age-related macular degeneration (AMD), atherosclerosis, and glaucoma. Manual diagnosis of retinal diseases by ophthalmologists takes time, effort, and financial resources, and is prone to error, in comparison to computer-aided diagnosis systems. In this context, robust classification and segmentation of retinal images are primary operations that aid clinicians in the early screening of patients to ensure the prevention and/or treatment of these diseases. This paper conducts an extensive review of the state-of-the-art methods for the detection and segmentation of retinal image features. Existing notable techniques for the detection of retinal features are categorized into essential groups and compared in depth. Additionally, a summary of quantifiable performance measures for various important stages of retinal image analysis, such as image acquisition and preprocessing, is provided. Finally, the widely used in the literature datasets for analyzing retinal images are described and their significance is emphasized.
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Affiliation(s)
- Shahzaib Iqbal
- Department of Electrical and Computer Engineering, COMSATS University Islamabad (CUI), Islamabad, Pakistan
| | - Tariq M Khan
- School of Computer Science and Engineering, University of New South Wales, Sydney, NSW, Australia.
| | - Khuram Naveed
- Department of Electrical and Computer Engineering, COMSATS University Islamabad (CUI), Islamabad, Pakistan; Department of Electrical and Computer Engineering, Aarhus University, Aarhus, Denmark
| | - Syed S Naqvi
- Department of Electrical and Computer Engineering, COMSATS University Islamabad (CUI), Islamabad, Pakistan
| | - Syed Junaid Nawaz
- Department of Electrical and Computer Engineering, COMSATS University Islamabad (CUI), Islamabad, Pakistan
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25
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Gojić G, Petrović VB, Dragan D, Gajić DB, Mišković D, Džinić V, Grgić Z, Pantelić J, Oros A. Comparing the Clinical Viability of Automated Fundus Image Segmentation Methods. SENSORS (BASEL, SWITZERLAND) 2022; 22:9101. [PMID: 36501801 PMCID: PMC9735987 DOI: 10.3390/s22239101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 11/16/2022] [Accepted: 11/17/2022] [Indexed: 06/17/2023]
Abstract
Recent methods for automatic blood vessel segmentation from fundus images have been commonly implemented as convolutional neural networks. While these networks report high values for objective metrics, the clinical viability of recovered segmentation masks remains unexplored. In this paper, we perform a pilot study to assess the clinical viability of automatically generated segmentation masks in the diagnosis of diseases affecting retinal vascularization. Five ophthalmologists with clinical experience were asked to participate in the study. The results demonstrate low classification accuracy, inferring that generated segmentation masks cannot be used as a standalone resource in general clinical practice. The results also hint at possible clinical infeasibility in experimental design. In the follow-up experiment, we evaluate the clinical quality of masks by having ophthalmologists rank generation methods. The ranking is established with high intra-observer consistency, indicating better subjective performance for a subset of tested networks. The study also demonstrates that objective metrics are not correlated with subjective metrics in retinal segmentation tasks for the methods involved, suggesting that objective metrics commonly used in scientific papers to measure the method's performance are not plausible criteria for choosing clinically robust solutions.
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Affiliation(s)
- Gorana Gojić
- The Institute for Artificial Intelligence Research and Development of Serbia, 21102 Novi Sad, Serbia
- Faculty of Technical Sciences, University of Novi Sad, 21102 Novi Sad, Serbia
| | - Veljko B. Petrović
- Faculty of Technical Sciences, University of Novi Sad, 21102 Novi Sad, Serbia
| | - Dinu Dragan
- Faculty of Technical Sciences, University of Novi Sad, 21102 Novi Sad, Serbia
| | - Dušan B. Gajić
- Faculty of Technical Sciences, University of Novi Sad, 21102 Novi Sad, Serbia
| | - Dragiša Mišković
- The Institute for Artificial Intelligence Research and Development of Serbia, 21102 Novi Sad, Serbia
| | | | | | - Jelica Pantelić
- Institute of Eye Diseases, University Clinical Center of Serbia, 11000 Belgrade, Serbia
| | - Ana Oros
- Eye Clinic Džinić, 21107 Novi Sad, Serbia
- Institute of Neonatology, 11000 Belgrade, Serbia
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Parashar D, Agrawal DK. Classification of Glaucoma Stages Using Image Empirical Mode Decomposition from Fundus Images. J Digit Imaging 2022; 35:1283-1292. [PMID: 35581407 PMCID: PMC9582090 DOI: 10.1007/s10278-022-00648-1] [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: 06/18/2020] [Revised: 03/18/2022] [Accepted: 04/03/2022] [Indexed: 11/29/2022] Open
Abstract
One of the most prevalent causes of visual loss and blindness is glaucoma. Conventionally, instrument-based tools are employed for glaucoma screening. However, they are inefficient, time-consuming, and manual. Hence, computerized methodologies are needed for fast and accurate diagnosis of glaucoma. Therefore, we proposed a Computer-Aided Diagnosis (CAD) method for the classification of glaucoma stages using Image Empirical Mode decomposition (IEMD). In this study, IEMD is applied to decompose the preprocessed fundus photographs into different Intrinsic Mode Functions (IMFs) to capture the pixel variations. Then, the significant texture-based descriptors have been computed from the IMFs. A dimensionality reduction approach called Principal Component Analysis (PCA) has been employed to pick the robust descriptors from the retrieved feature set. We used the Analysis of Variance (ANOVA) test for feature ranking. Finally, the LS-SVM classifier has been employed to classify glaucoma stages. The proposed CAD system achieved a classification accuracy of 94.45% for the binary classification on the RIM-ONE r12 database. Our approach demonstrated better glaucoma classification performance than the existing automated systems.
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Affiliation(s)
- Deepak Parashar
- Department of Electronics and Communication Engineering, IES College of Technology, Bhopal, 462044, MP, India.
- Department of Electronics and Communication Engineering, Maulana Azad National Institute of Technology, Bhopal, 462003, MP, India.
| | - Dheraj Kumar Agrawal
- Department of Electronics and Communication Engineering, Maulana Azad National Institute of Technology, Bhopal, 462003, MP, India
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Zhang Z, Cheng N, Liu Y, Song J, Liu X, Zhang S, Zhang G. Prediction of corneal astigmatism based on corneal tomography after femtosecond laser arcuate keratotomy using a pix2pix conditional generative adversarial network. Front Public Health 2022; 10:1012929. [PMID: 36187623 PMCID: PMC9523441 DOI: 10.3389/fpubh.2022.1012929] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2022] [Accepted: 08/29/2022] [Indexed: 01/27/2023] Open
Abstract
Purpose This study aimed to develop a deep learning model to generate a postoperative corneal axial curvature map of femtosecond laser arcuate keratotomy (FLAK) based on corneal tomography using a pix2pix conditional generative adversarial network (pix2pix cGAN) for surgical planning. Methods A total of 451 eyes of 318 nonconsecutive patients were subjected to FLAK for corneal astigmatism correction during cataract surgery. Paired or single anterior penetrating FLAKs were performed at an 8.0-mm optical zone with a depth of 90% using a femtosecond laser (LenSx laser, Alcon Laboratories, Inc.). Corneal tomography images were acquired from Oculus Pentacam HR (Optikgeräte GmbH, Wetzlar, Germany) before and 3 months after the surgery. The raw data required for analysis consisted of the anterior corneal curvature for a range of ± 3.5 mm around the corneal apex in 0.1-mm steps, which the pseudo-color corneal curvature map synthesized was based on. The deep learning model used was a pix2pix conditional generative adversarial network. The prediction accuracy of synthetic postoperative corneal astigmatism in zones of different diameters centered on the corneal apex was assessed using vector analysis. The synthetic postoperative corneal axial curvature maps were compared with the real postoperative corneal axial curvature maps using the structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR). Results A total of 386 pairs of preoperative and postoperative corneal tomography data were included in the training set, whereas 65 preoperative data were retrospectively included in the test set. The correlation coefficient between synthetic and real postoperative astigmatism (difference vector) in the 3-mm zone was 0.89, and that between surgically induced astigmatism (SIA) was 0.93. The mean absolute errors of SIA for real and synthetic postoperative corneal axial curvature maps in the 1-, 3-, and 5-mm zone were 0.20 ± 0.25, 0.12 ± 0.17, and 0.09 ± 0.13 diopters, respectively. The average SSIM and PSNR of the 3-mm zone were 0.86 ± 0.04 and 18.24 ± 5.78, respectively. Conclusion Our results showed that the application of pix2pix cGAN can synthesize plausible postoperative corneal tomography for FLAK, showing the possibility of using GAN to predict corneal tomography, with the potential of applying artificial intelligence to construct surgical planning models.
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Affiliation(s)
- Zhe Zhang
- Shenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen, China,Department of Cataract, Shanxi Eye Hospital, Taiyuan, China,First Hospital of Shanxi Medical University, Taiyuan, China
| | - Nan Cheng
- College of Biomedical Engineering, Taiyuan University of Technology, Taiyuan, China
| | - Yunfang Liu
- Department of Ophthalmology, First Affiliated Hospital of Huzhou University, Huzhou, China
| | - Junyang Song
- Department of Ophthalmology, First Affiliated Hospital of Huzhou University, Huzhou, China,*Correspondence: Junyang Song
| | - Xinhua Liu
- Shenzhen Eye Hospital, Jinan University, Shenzhen Eye Institute, Shenzhen, China
| | - Suhua Zhang
- Department of Cataract, Shanxi Eye Hospital, Taiyuan, China,Taiyuan Central Hospital of Shanxi Medical University, Taiyuan, China,Suhua Zhang
| | - Guanghua Zhang
- Department of Intelligence and Automation, Taiyuan University, Taiyuan, China,Graphics and Imaging Laboratory, University of Girona, Girona, Spain,Guanghua Zhang
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Guo S. CSGNet: Cascade semantic guided net for retinal vessel segmentation. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Yue C, Ye M, Wang P, Huang D, Lu X. SRV-GAN: A generative adversarial network for segmenting retinal vessels. MATHEMATICAL BIOSCIENCES AND ENGINEERING : MBE 2022; 19:9948-9965. [PMID: 36031977 DOI: 10.3934/mbe.2022464] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
In the field of ophthalmology, retinal diseases are often accompanied by complications, and effective segmentation of retinal blood vessels is an important condition for judging retinal diseases. Therefore, this paper proposes a segmentation model for retinal blood vessel segmentation. Generative adversarial networks (GANs) have been used for image semantic segmentation and show good performance. So, this paper proposes an improved GAN. Based on R2U-Net, the generator adds an attention mechanism, channel and spatial attention, which can reduce the loss of information and extract more effective features. We use dense connection modules in the discriminator. The dense connection module has the characteristics of alleviating gradient disappearance and realizing feature reuse. After a certain amount of iterative training, the generated prediction map and label map can be distinguished. Based on the loss function in the traditional GAN, we introduce the mean squared error. By using this loss, we ensure that the synthetic images contain more realistic blood vessel structures. The values of area under the curve (AUC) in the retinal blood vessel pixel segmentation of the three public data sets DRIVE, CHASE-DB1 and STARE of the proposed method are 0.9869, 0.9894 and 0.9885, respectively. The indicators of this experiment have improved compared to previous methods.
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Affiliation(s)
- Chen Yue
- School of Medical Information, Wannan Medical College, Wuhu 241002, China
- Research Center of Health Big Data Mining and Applications, Wannan Medical College, Wuhu 241002, China
| | - Mingquan Ye
- School of Medical Information, Wannan Medical College, Wuhu 241002, China
- Research Center of Health Big Data Mining and Applications, Wannan Medical College, Wuhu 241002, China
| | - Peipei Wang
- School of Medical Information, Wannan Medical College, Wuhu 241002, China
- Research Center of Health Big Data Mining and Applications, Wannan Medical College, Wuhu 241002, China
| | - Daobin Huang
- School of Medical Information, Wannan Medical College, Wuhu 241002, China
- Research Center of Health Big Data Mining and Applications, Wannan Medical College, Wuhu 241002, China
| | - Xiaojie Lu
- School of Medical Information, Wannan Medical College, Wuhu 241002, China
- Research Center of Health Big Data Mining and Applications, Wannan Medical College, Wuhu 241002, China
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Abstract
Topological and geometrical analysis of retinal blood vessels could be a cost-effective way to detect various common diseases. Automated vessel segmentation and vascular tree analysis models require powerful generalization capability in clinical applications. In this work, we constructed a novel benchmark RETA with 81 labelled vessel masks aiming to facilitate retinal vessel analysis. A semi-automated coarse-to-fine workflow was proposed for vessel annotation task. During database construction, we strived to control inter-annotator and intra-annotator variability by means of multi-stage annotation and label disambiguation on self-developed dedicated software. In addition to binary vessel masks, we obtained other types of annotations including artery/vein masks, vascular skeletons, bifurcations, trees and abnormalities. Subjective and objective quality validations of the annotated vessel masks demonstrated significantly improved quality over the existing open datasets. Our annotation software is also made publicly available serving the purpose of pixel-level vessel visualization. Researchers could develop vessel segmentation algorithms and evaluate segmentation performance using RETA. Moreover, it might promote the study of cross-modality tubular structure segmentation and analysis.
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31
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Iqbal A, Sharif M, Yasmin M, Raza M, Aftab S. Generative adversarial networks and its applications in the biomedical image segmentation: a comprehensive survey. INTERNATIONAL JOURNAL OF MULTIMEDIA INFORMATION RETRIEVAL 2022; 11:333-368. [PMID: 35821891 PMCID: PMC9264294 DOI: 10.1007/s13735-022-00240-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Revised: 03/16/2022] [Accepted: 05/24/2022] [Indexed: 05/13/2023]
Abstract
Recent advancements with deep generative models have proven significant potential in the task of image synthesis, detection, segmentation, and classification. Segmenting the medical images is considered a primary challenge in the biomedical imaging field. There have been various GANs-based models proposed in the literature to resolve medical segmentation challenges. Our research outcome has identified 151 papers; after the twofold screening, 138 papers are selected for the final survey. A comprehensive survey is conducted on GANs network application to medical image segmentation, primarily focused on various GANs-based models, performance metrics, loss function, datasets, augmentation methods, paper implementation, and source codes. Secondly, this paper provides a detailed overview of GANs network application in different human diseases segmentation. We conclude our research with critical discussion, limitations of GANs, and suggestions for future directions. We hope this survey is beneficial and increases awareness of GANs network implementations for biomedical image segmentation tasks.
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Affiliation(s)
- Ahmed Iqbal
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Pakistan
| | - Muhammad Sharif
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Pakistan
| | - Mussarat Yasmin
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Pakistan
| | - Mudassar Raza
- Department of Computer Science, COMSATS University Islamabad, Wah Campus, Pakistan
| | - Shabib Aftab
- Department of Computer Science, Virtual University of Pakistan, Lahore, Pakistan
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32
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Pasupathy V, Khilar R. Advancements in deep structured learning based medical image interpretation. JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES 2022. [DOI: 10.1080/02522667.2022.2094550] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/13/2023]
Affiliation(s)
- Vijayalakshmi Pasupathy
- Department of Computer Science and Engineering, Panimalar Engineering College, Chennai, Tamil Nadu, India
| | - Rashmita Khilar
- Department of Information Technology, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nadu, India
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Joshi A, Sharma KK. Graph deep network for optic disc and optic cup segmentation for glaucoma disease using retinal imaging. Phys Eng Sci Med 2022; 45:847-858. [PMID: 35737221 DOI: 10.1007/s13246-022-01154-y] [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: 03/25/2022] [Accepted: 06/07/2022] [Indexed: 11/25/2022]
Abstract
The fundus imaging method of eye screening detects eye diseases by segmenting the optic disc (OD) and optic cup (OC). OD and OC are still challenging to segment accurately. This work proposes three-layer graph-based deep architecture with an enhanced fusion method for OD and OC segmentation. CNN encoder-decoder architecture, extended graph network, and approximation via fusion-based rule are explored for connecting local and global information. A graph-based model is developed for combining local and overall knowledge. By extending feature masking, regularization of repetitive features with fusion for combining channels has been done. The performance of the proposed network is evaluated through the analysis of different metric parameters such as dice similarity coefficient (DSC), intersection of union (IOU), accuracy, specificity, sensitivity. Experimental verification of this methodology has been done using the four benchmarks publicly available datasets DRISHTI-GS, RIM-ONE for OD, and OC segmentation. In addition, DRIONS-DB and HRF fundus imaging datasets were analyzed for optimizing the model's performance based on OD segmentation. DSC metric of methodology achieved 0.97 and 0.96 for DRISHTI-GS and RIM-ONE, respectively. Similarly, IOU measures for DRISHTI-GS and RIM-ONE datasets were 0.96 and 0.93, respectively, for OD measurement. For OC segmentation, DSC and IOU were measured as 0.93 and 0.90 respectively for DRISHTI-GS and 0.83 and 0.82 for RIM-ONE data. The proposed technique improved value of metrics with most of the existing methods in terms of DSC and IOU of the results metric of the experiments for OD and OC segmentation.
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Affiliation(s)
- Abhilasha Joshi
- Electronics and Communication Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, 302017, India.
| | - K K Sharma
- Electronics and Communication Engineering, Malaviya National Institute of Technology, Jaipur, Rajasthan, 302017, India
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34
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Wang Y, Yu X, Wu C. An Efficient Hierarchical Optic Disc and Cup Segmentation Network Combined with Multi-task Learning and Adversarial Learning. J Digit Imaging 2022; 35:638-653. [PMID: 35212860 PMCID: PMC9156633 DOI: 10.1007/s10278-021-00579-3] [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: 09/02/2021] [Revised: 12/24/2021] [Accepted: 12/29/2021] [Indexed: 12/15/2022] Open
Abstract
Automatic and accurate segmentation of optic disc (OD) and optic cup (OC) in fundus images is a fundamental task in computer-aided ocular pathologies diagnosis. The complex structures, such as blood vessels and macular region, and the existence of lesions in fundus images bring great challenges to the segmentation task. Recently, the convolutional neural network-based methods have exhibited its potential in fundus image analysis. In this paper, we propose a cascaded two-stage network architecture for robust and accurate OD and OC segmentation in fundus images. In the first stage, the U-Net like framework with an improved attention mechanism and focal loss is proposed to detect accurate and reliable OD location from the full-scale resolution fundus images. Based on the outputs of the first stage, a refined segmentation network in the second stage that integrates multi-task framework and adversarial learning is further designed for OD and OC segmentation separately. The multi-task framework is conducted to predict the OD and OC masks by simultaneously estimating contours and distance maps as auxiliary tasks, which can guarantee the smoothness and shape of object in segmentation predictions. The adversarial learning technique is introduced to encourage the segmentation network to produce an output that is consistent with the true labels in space and shape distribution. We evaluate the performance of our method using two public retinal fundus image datasets (RIM-ONE-r3 and REFUGE). Extensive ablation studies and comparison experiments with existing methods demonstrate that our approach can produce competitive performance compared with state-of-the-art methods.
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Affiliation(s)
- Ying Wang
- grid.412252.20000 0004 0368 6968College of Information Science and Engineering, Northeastern University, Liaoning, 110819 China
| | - Xiaosheng Yu
- grid.412252.20000 0004 0368 6968Faculty of Robot Science and Engineering, Northeastern University, Liaoning, 110819 China
| | - Chengdong Wu
- grid.412252.20000 0004 0368 6968Faculty of Robot Science and Engineering, Northeastern University, Liaoning, 110819 China
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35
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An innovative medical image synthesis based on dual GAN deep neural networks for improved segmentation quality. APPL INTELL 2022. [DOI: 10.1007/s10489-022-03682-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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36
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Hofer D, Schmidt-Erfurth U, Orlando JI, Goldbach F, Gerendas BS, Seeböck P. Improving foveal avascular zone segmentation in fluorescein angiograms by leveraging manual vessel labels from public color fundus pictures. BIOMEDICAL OPTICS EXPRESS 2022; 13:2566-2580. [PMID: 35774310 PMCID: PMC9203117 DOI: 10.1364/boe.452873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 03/11/2022] [Accepted: 03/24/2022] [Indexed: 06/15/2023]
Abstract
In clinical routine, ophthalmologists frequently analyze the shape and size of the foveal avascular zone (FAZ) to detect and monitor retinal diseases. In order to extract those parameters, the contours of the FAZ need to be segmented, which is normally achieved by analyzing the retinal vasculature (RV) around the macula in fluorescein angiograms (FA). Computer-aided segmentation methods based on deep learning (DL) can automate this task. However, current approaches for segmenting the FAZ are often tailored to a specific dataset or require manual initialization. Furthermore, they do not take the variability and challenges of clinical FA into account, which are often of low quality and difficult to analyze. In this paper we propose a DL-based framework to automatically segment the FAZ in challenging FA scans from clinical routine. Our approach mimics the workflow of retinal experts by using additional RV labels as a guidance during training. Hence, our model is able to produce RV segmentations simultaneously. We minimize the annotation work by using a multi-modal approach that leverages already available public datasets of color fundus pictures (CFPs) and their respective manual RV labels. Our experimental evaluation on two datasets with FA from 1) clinical routine and 2) large multicenter clinical trials shows that the addition of weak RV labels as a guidance during training improves the FAZ segmentation significantly with respect to using only manual FAZ annotations.
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Affiliation(s)
- Dominik Hofer
- Vienna Reading Center, Department of Ophthalmology and Optometry, Medical University of Vienna, Spitalgasse 23, 1090 Vienna, Austria
| | - Ursula Schmidt-Erfurth
- Vienna Reading Center, Department of Ophthalmology and Optometry, Medical University of Vienna, Spitalgasse 23, 1090 Vienna, Austria
| | - José Ignacio Orlando
- Vienna Reading Center, Department of Ophthalmology and Optometry, Medical University of Vienna, Spitalgasse 23, 1090 Vienna, Austria
- Yatiris Group, PLADEMA Institute, CON-ICET, Universidad Nacional del Centro de la Provincia de Buenos Aires, Gral. Pinto 399, Tandil, Buenos Aires, Argentina
| | - Felix Goldbach
- Vienna Reading Center, Department of Ophthalmology and Optometry, Medical University of Vienna, Spitalgasse 23, 1090 Vienna, Austria
| | - Bianca S. Gerendas
- Vienna Reading Center, Department of Ophthalmology and Optometry, Medical University of Vienna, Spitalgasse 23, 1090 Vienna, Austria
| | - Philipp Seeböck
- Vienna Reading Center, Department of Ophthalmology and Optometry, Medical University of Vienna, Spitalgasse 23, 1090 Vienna, Austria
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Lim JS, Hong M, Lam WST, Zhang Z, Teo ZL, Liu Y, Ng WY, Foo LL, Ting DSW. Novel technical and privacy-preserving technology for artificial intelligence in ophthalmology. Curr Opin Ophthalmol 2022; 33:174-187. [PMID: 35266894 DOI: 10.1097/icu.0000000000000846] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
PURPOSE OF REVIEW The application of artificial intelligence (AI) in medicine and ophthalmology has experienced exponential breakthroughs in recent years in diagnosis, prognosis, and aiding clinical decision-making. The use of digital data has also heralded the need for privacy-preserving technology to protect patient confidentiality and to guard against threats such as adversarial attacks. Hence, this review aims to outline novel AI-based systems for ophthalmology use, privacy-preserving measures, potential challenges, and future directions of each. RECENT FINDINGS Several key AI algorithms used to improve disease detection and outcomes include: Data-driven, imagedriven, natural language processing (NLP)-driven, genomics-driven, and multimodality algorithms. However, deep learning systems are susceptible to adversarial attacks, and use of data for training models is associated with privacy concerns. Several data protection methods address these concerns in the form of blockchain technology, federated learning, and generative adversarial networks. SUMMARY AI-applications have vast potential to meet many eyecare needs, consequently reducing burden on scarce healthcare resources. A pertinent challenge would be to maintain data privacy and confidentiality while supporting AI endeavors, where data protection methods would need to rapidly evolve with AI technology needs. Ultimately, for AI to succeed in medicine and ophthalmology, a balance would need to be found between innovation and privacy.
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Affiliation(s)
- Jane S Lim
- Singapore National Eye Centre, Singapore Eye Research Institute
| | | | - Walter S T Lam
- Yong Loo Lin School of Medicine, National University of Singapore
| | - Zheting Zhang
- Lee Kong Chian School of Medicine, Nanyang Technological University
| | - Zhen Ling Teo
- Singapore National Eye Centre, Singapore Eye Research Institute
| | - Yong Liu
- National University of Singapore, DukeNUS Medical School, Singapore
| | - Wei Yan Ng
- Singapore National Eye Centre, Singapore Eye Research Institute
| | - Li Lian Foo
- Singapore National Eye Centre, Singapore Eye Research Institute
| | - Daniel S W Ting
- Singapore National Eye Centre, Singapore Eye Research Institute
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38
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State-of-the-art retinal vessel segmentation with minimalistic models. Sci Rep 2022; 12:6174. [PMID: 35418576 PMCID: PMC9007957 DOI: 10.1038/s41598-022-09675-y] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Accepted: 03/10/2022] [Indexed: 01/03/2023] Open
Abstract
The segmentation of retinal vasculature from eye fundus images is a fundamental task in retinal image analysis. Over recent years, increasingly complex approaches based on sophisticated Convolutional Neural Network architectures have been pushing performance on well-established benchmark datasets. In this paper, we take a step back and analyze the real need of such complexity. We first compile and review the performance of 20 different techniques on some popular databases, and we demonstrate that a minimalistic version of a standard U-Net with several orders of magnitude less parameters, carefully trained and rigorously evaluated, closely approximates the performance of current best techniques. We then show that a cascaded extension (W-Net) reaches outstanding performance on several popular datasets, still using orders of magnitude less learnable weights than any previously published work. Furthermore, we provide the most comprehensive cross-dataset performance analysis to date, involving up to 10 different databases. Our analysis demonstrates that the retinal vessel segmentation is far from solved when considering test images that differ substantially from the training data, and that this task represents an ideal scenario for the exploration of domain adaptation techniques. In this context, we experiment with a simple self-labeling strategy that enables moderate enhancement of cross-dataset performance, indicating that there is still much room for improvement in this area. Finally, we test our approach on Artery/Vein and vessel segmentation from OCTA imaging problems, where we again achieve results well-aligned with the state-of-the-art, at a fraction of the model complexity available in recent literature. Code to reproduce the results in this paper is released.
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Lin G, Bai H, Zhao J, Yun Z, Chen Y, Pang S, Feng Q. Improving sensitivity and connectivity of retinal vessel segmentation via error discrimination network. Med Phys 2022; 49:4494-4507. [PMID: 35338781 DOI: 10.1002/mp.15627] [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: 11/10/2021] [Revised: 03/04/2022] [Accepted: 03/08/2022] [Indexed: 11/09/2022] Open
Abstract
PURPOSE Automated retinal vessel segmentation is crucial to the early diagnosis and treatment of ophthalmological diseases. Many deep learning-based methods have shown exceptional success in this task. However, current approaches are still inadequate in challenging vessels (e.g., thin vessels) and rarely focus on the connectivity of vessel segmentation. METHODS We propose using an error discrimination network (D) to distinguish whether the vessel pixel predictions of the segmentation network (S) are correct, and S is trained to obtain fewer error predictions of D. Our method is similar to, but not the same as, the generative adversarial network (GAN). Three types of vessel samples and corresponding error masks are used to train D, as follows: (1) vessel ground truth; (2) vessel segmented by S; (3) artificial thin vessel error samples that further improve the sensitivity of D to wrong small vessels. As an auxiliary loss function of S, D strengthens the supervision of difficult vessels. Optionally, we can use the errors predicted by D to correct the segmentation result of S. RESULTS Compared with state-of-the-art methods, our method achieves the highest scores in sensitivity (86.19%, 86.26%, and 86.53%) and G-Mean (91.94%, 91.30%, and 92.76%) on three public datasets, namely, STARE, DRIVE, and HRF. Our method also maintains a competitive level in other metrics. On the STARE dataset, the F1-score and AUC of our method rank second and first, respectively, reaching 84.51% and 98.97%. The top scores of the three topology-relevant metrics (Conn, Inf, and Cor) demonstrate that the vessels extracted by our method have excellent connectivity. We also validate the effectiveness of error discrimination supervision and artificial error sample training through ablation experiments. CONCLUSIONS The proposed method provides an accurate and robust solution for difficult vessel segmentation. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Guoye Lin
- Guangdong Provincial Key Laboratory of Medical Image Processing, Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China
| | - Hanhua Bai
- Guangdong Provincial Key Laboratory of Medical Image Processing, Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China
| | - Jie Zhao
- Guangdong Provincial Key Laboratory of Medical Image Processing, Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China.,School of Medical Information Engineering, Guangdong Pharmaceutical University, Guangzhou, Guangdong, China
| | - Zhaoqiang Yun
- Guangdong Provincial Key Laboratory of Medical Image Processing, Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China
| | - Yangfan Chen
- Guangdong Provincial Key Laboratory of Medical Image Processing, Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China
| | - Shumao Pang
- Guangdong Provincial Key Laboratory of Medical Image Processing, Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China
| | - Qianjin Feng
- Guangdong Provincial Key Laboratory of Medical Image Processing, Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, School of Biomedical Engineering, Southern Medical University, Guangzhou, Guangdong, China
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Alawad M, Aljouie A, Alamri S, Alghamdi M, Alabdulkader B, Alkanhal N, Almazroa A. Machine Learning and Deep Learning Techniques for Optic Disc and Cup Segmentation – A Review. Clin Ophthalmol 2022; 16:747-764. [PMID: 35300031 PMCID: PMC8923700 DOI: 10.2147/opth.s348479] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 02/11/2022] [Indexed: 12/12/2022] Open
Abstract
Background Globally, glaucoma is the second leading cause of blindness. Detecting glaucoma in the early stages is essential to avoid disease complications, which lead to blindness. Thus, computer-aided diagnosis systems are powerful tools to overcome the shortage of glaucoma screening programs. Methods A systematic search of public databases, including PubMed, Google Scholar, and other sources, was performed to identify relevant studies to overview the publicly available fundus image datasets used to train, validate, and test machine learning and deep learning methods. Additionally, existing machine learning and deep learning methods for optic cup and disc segmentation were surveyed and critically reviewed. Results Eight fundus images datasets were publicly available with 15,445 images labeled with glaucoma or non-glaucoma, and manually annotated optic disc and cup boundaries were found. Five metrics were identified for evaluating the developed models. Finally, three main deep learning architectural designs were commonly used for optic disc and optic cup segmentation. Conclusion We provided future research directions to formulate robust optic cup and disc segmentation systems. Deep learning can be utilized in clinical settings for this task. However, many challenges need to be addressed before using this strategy in clinical trials. Finally, two deep learning architectural designs have been widely adopted, such as U-net and its variants.
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Affiliation(s)
- Mohammed Alawad
- Department of Biostatistics and Bioinformatics, King Abdullah International Medical Research Center, King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
| | - Abdulrhman Aljouie
- Department of Biostatistics and Bioinformatics, King Abdullah International Medical Research Center, King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
| | - Suhailah Alamri
- Department of Imaging Research, King Abdullah International Medical Research Center, King Saud bin Abdulaziz University for health Sciences, Riyadh, Saudi Arabia
- Research Labs, National Center for Artificial Intelligence, Riyadh, Saudi Arabia
| | - Mansour Alghamdi
- Department of Optometry and Vision Sciences College of Applied Medical Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Balsam Alabdulkader
- Department of Optometry and Vision Sciences College of Applied Medical Sciences, King Saud University, Riyadh, Saudi Arabia
| | - Norah Alkanhal
- Department of Imaging Research, King Abdullah International Medical Research Center, King Saud bin Abdulaziz University for health Sciences, Riyadh, Saudi Arabia
| | - Ahmed Almazroa
- Department of Imaging Research, King Abdullah International Medical Research Center, King Saud bin Abdulaziz University for health Sciences, Riyadh, Saudi Arabia
- Correspondence: Ahmed Almazroa; Abdulrhman Aljouie, Email ;
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41
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You A, Kim JK, Ryu IH, Yoo TK. Application of generative adversarial networks (GAN) for ophthalmology image domains: a survey. EYE AND VISION (LONDON, ENGLAND) 2022; 9:6. [PMID: 35109930 PMCID: PMC8808986 DOI: 10.1186/s40662-022-00277-3] [Citation(s) in RCA: 48] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Accepted: 01/11/2022] [Indexed: 12/12/2022]
Abstract
BACKGROUND Recent advances in deep learning techniques have led to improved diagnostic abilities in ophthalmology. A generative adversarial network (GAN), which consists of two competing types of deep neural networks, including a generator and a discriminator, has demonstrated remarkable performance in image synthesis and image-to-image translation. The adoption of GAN for medical imaging is increasing for image generation and translation, but it is not familiar to researchers in the field of ophthalmology. In this work, we present a literature review on the application of GAN in ophthalmology image domains to discuss important contributions and to identify potential future research directions. METHODS We performed a survey on studies using GAN published before June 2021 only, and we introduced various applications of GAN in ophthalmology image domains. The search identified 48 peer-reviewed papers in the final review. The type of GAN used in the analysis, task, imaging domain, and the outcome were collected to verify the usefulness of the GAN. RESULTS In ophthalmology image domains, GAN can perform segmentation, data augmentation, denoising, domain transfer, super-resolution, post-intervention prediction, and feature extraction. GAN techniques have established an extension of datasets and modalities in ophthalmology. GAN has several limitations, such as mode collapse, spatial deformities, unintended changes, and the generation of high-frequency noises and artifacts of checkerboard patterns. CONCLUSIONS The use of GAN has benefited the various tasks in ophthalmology image domains. Based on our observations, the adoption of GAN in ophthalmology is still in a very early stage of clinical validation compared with deep learning classification techniques because several problems need to be overcome for practical use. However, the proper selection of the GAN technique and statistical modeling of ocular imaging will greatly improve the performance of each image analysis. Finally, this survey would enable researchers to access the appropriate GAN technique to maximize the potential of ophthalmology datasets for deep learning research.
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Affiliation(s)
- Aram You
- School of Architecture, Kumoh National Institute of Technology, Gumi, Gyeongbuk, South Korea
| | - Jin Kuk Kim
- B&VIIT Eye Center, Seoul, South Korea
- VISUWORKS, Seoul, South Korea
| | - Ik Hee Ryu
- B&VIIT Eye Center, Seoul, South Korea
- VISUWORKS, Seoul, South Korea
| | - Tae Keun Yoo
- B&VIIT Eye Center, Seoul, South Korea.
- Department of Ophthalmology, Aerospace Medical Center, Republic of Korea Air Force, 635 Danjae-ro, Namil-myeon, Cheongwon-gun, Cheongju, Chungcheongbuk-do, 363-849, South Korea.
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Badawi SA, Fraz MM, Shehzad M, Mahmood I, Javed S, Mosalam E, Nileshwar AK. Detection and Grading of Hypertensive Retinopathy Using Vessels Tortuosity and Arteriovenous Ratio. J Digit Imaging 2022; 35:281-301. [PMID: 35013827 PMCID: PMC8921404 DOI: 10.1007/s10278-021-00545-z] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2020] [Revised: 11/09/2021] [Accepted: 11/11/2021] [Indexed: 10/19/2022] Open
Abstract
Hypertensive retinopathy (HR) refers to changes in the morphological diameter of the retinal vessels due to persistent high blood pressure. Early detection of such changes helps in preventing blindness or even death due to stroke. These changes can be quantified by computing the arteriovenous ratio and the tortuosity severity in the retinal vasculature. This paper presents a decision support system for detecting and grading HR using morphometric analysis of retinal vasculature, particularly measuring the arteriovenous ratio (AVR) and retinal vessel tortuosity. In the first step, the retinal blood vessels are segmented and classified as arteries and veins. Then, the width of arteries and veins is measured within the region of interest around the optic disk. Next, a new iterative method is proposed to compute the AVR from the caliber measurements of arteries and veins using Parr-Hubbard and Knudtson methods. Moreover, the retinal vessel tortuosity severity index is computed for each image using 14 tortuosity severity metrics. In the end, a hybrid decision support system is proposed for the detection and grading of HR using AVR and tortuosity severity index. Furthermore, we present a new publicly available retinal vessel morphometry (RVM) dataset to evaluate the proposed methodology. The RVM dataset contains 504 retinal images with pixel-level annotations for vessel segmentation, artery/vein classification, and optic disk localization. The image-level labels for vessel tortuosity index and HR grade are also available. The proposed methods of iterative AVR measurement, tortuosity index, and HR grading are evaluated using the new RVM dataset. The results indicate that the proposed method gives superior performance than existing methods. The presented methodology is a novel advancement in automated detection and grading of HR, which can potentially be used as a clinical decision support system.
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Affiliation(s)
- Sufian A Badawi
- School of Electrical Engineering and Computer Science, National University of Sciences and Technology (NUST), Islamabad, Pakistan
| | - Muhammad Moazam Fraz
- School of Electrical Engineering and Computer Science, National University of Sciences and Technology (NUST), Islamabad, Pakistan.
| | - Muhammad Shehzad
- School of Electrical Engineering and Computer Science, National University of Sciences and Technology (NUST), Islamabad, Pakistan
| | - Imran Mahmood
- School of Electrical Engineering and Computer Science, National University of Sciences and Technology (NUST), Islamabad, Pakistan
| | - Sajid Javed
- Department of Electrical Engineering and Computer Science, Khalifa University, Abu Dhabi, UAE
| | - Emad Mosalam
- Department of Ophthalmology, RAK Medical and Health Sciences University, Ras Al Khaimah, UAE.,Department of Ophthalmology, Saqr Hospital, Ministry of Health and Prevention, Ras Al Khaimah, UAE
| | - Ajay Kamath Nileshwar
- Department of Ophthalmology, RAK Medical and Health Sciences University, Ras Al Khaimah, UAE.,Department of Ophthalmology, Saqr Hospital, Ministry of Health and Prevention, Ras Al Khaimah, UAE
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43
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Xun S, Li D, Zhu H, Chen M, Wang J, Li J, Chen M, Wu B, Zhang H, Chai X, Jiang Z, Zhang Y, Huang P. Generative adversarial networks in medical image segmentation: A review. Comput Biol Med 2022; 140:105063. [PMID: 34864584 DOI: 10.1016/j.compbiomed.2021.105063] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Revised: 11/14/2021] [Accepted: 11/20/2021] [Indexed: 12/13/2022]
Abstract
PURPOSE Since Generative Adversarial Network (GAN) was introduced into the field of deep learning in 2014, it has received extensive attention from academia and industry, and a lot of high-quality papers have been published. GAN effectively improves the accuracy of medical image segmentation because of its good generating ability and capability to capture data distribution. This paper introduces the origin, working principle, and extended variant of GAN, and it reviews the latest development of GAN-based medical image segmentation methods. METHOD To find the papers, we searched on Google Scholar and PubMed with the keywords like "segmentation", "medical image", and "GAN (or generative adversarial network)". Also, additional searches were performed on Semantic Scholar, Springer, arXiv, and the top conferences in computer science with the above keywords related to GAN. RESULTS We reviewed more than 120 GAN-based architectures for medical image segmentation that were published before September 2021. We categorized and summarized these papers according to the segmentation regions, imaging modality, and classification methods. Besides, we discussed the advantages, challenges, and future research directions of GAN in medical image segmentation. CONCLUSIONS We discussed in detail the recent papers on medical image segmentation using GAN. The application of GAN and its extended variants has effectively improved the accuracy of medical image segmentation. Obtaining the recognition of clinicians and patients and overcoming the instability, low repeatability, and uninterpretability of GAN will be an important research direction in the future.
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Affiliation(s)
- Siyi Xun
- Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine, School of Physics and Electronics, Shandong Normal University, Jinan, Shandong, 250358, China
| | - Dengwang Li
- Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine, School of Physics and Electronics, Shandong Normal University, Jinan, Shandong, 250358, China.
| | - Hui Zhu
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, Shandong, China
| | - Min Chen
- The Second Hospital of Shandong University, Shandong University, The Department of Medicine, The Second Hospital of Shandong University, Jinan, China
| | - Jianbo Wang
- Department of Radiation Oncology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, 250012, China
| | - Jie Li
- Department of Infectious Disease, Shandong Provincial Hospital Affiliated to Shandong University, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
| | - Meirong Chen
- Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
| | - Bing Wu
- Laibo Biotechnology Co., Ltd., Jinan, Shandong, China
| | - Hua Zhang
- LinkingMed Technology Co., Ltd., Beijing, China
| | - Xiangfei Chai
- Huiying Medical Technology (Beijing) Co., Ltd., Beijing, China
| | - Zekun Jiang
- Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine, School of Physics and Electronics, Shandong Normal University, Jinan, Shandong, 250358, China
| | - Yan Zhang
- Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine, School of Physics and Electronics, Shandong Normal University, Jinan, Shandong, 250358, China
| | - Pu Huang
- Shandong Key Laboratory of Medical Physics and Image Processing, Shandong Institute of Industrial Technology for Health Sciences and Precision Medicine, School of Physics and Electronics, Shandong Normal University, Jinan, Shandong, 250358, China.
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Yousef R, Gupta G, Yousef N, Khari M. A holistic overview of deep learning approach in medical imaging. MULTIMEDIA SYSTEMS 2022; 28:881-914. [PMID: 35079207 PMCID: PMC8776556 DOI: 10.1007/s00530-021-00884-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/09/2021] [Accepted: 12/23/2021] [Indexed: 05/07/2023]
Abstract
Medical images are a rich source of invaluable necessary information used by clinicians. Recent technologies have introduced many advancements for exploiting the most of this information and use it to generate better analysis. Deep learning (DL) techniques have been empowered in medical images analysis using computer-assisted imaging contexts and presenting a lot of solutions and improvements while analyzing these images by radiologists and other specialists. In this paper, we present a survey of DL techniques used for variety of tasks along with the different medical image's modalities to provide critical review of the recent developments in this direction. We have organized our paper to provide significant contribution of deep leaning traits and learn its concepts, which is in turn helpful for non-expert in medical society. Then, we present several applications of deep learning (e.g., segmentation, classification, detection, etc.) which are commonly used for clinical purposes for different anatomical site, and we also present the main key terms for DL attributes like basic architecture, data augmentation, transfer learning, and feature selection methods. Medical images as inputs to deep learning architectures will be the mainstream in the coming years, and novel DL techniques are predicted to be the core of medical images analysis. We conclude our paper by addressing some research challenges and the suggested solutions for them found in literature, and also future promises and directions for further developments.
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Affiliation(s)
- Rammah Yousef
- Yogananda School of AI Computer and Data Sciences, Shoolini University, Solan, 173229 Himachal Pradesh India
| | - Gaurav Gupta
- Yogananda School of AI Computer and Data Sciences, Shoolini University, Solan, 173229 Himachal Pradesh India
| | - Nabhan Yousef
- Electronics and Communication Engineering, Marwadi University, Rajkot, Gujrat India
| | - Manju Khari
- Jawaharlal Nehru University, New Delhi, India
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45
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Zhan Q, Liu Y, Liu Y, Hu W. Frontal Cortex Segmentation of Brain PET Imaging Using Deep Neural Networks. Front Neurosci 2021; 15:796172. [PMID: 34955739 PMCID: PMC8694272 DOI: 10.3389/fnins.2021.796172] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Accepted: 11/01/2021] [Indexed: 11/13/2022] Open
Abstract
18F-FDG positron emission tomography (PET) imaging of brain glucose use and amyloid accumulation is a research criteria for Alzheimer's disease (AD) diagnosis. Several PET studies have shown widespread metabolic deficits in the frontal cortex for AD patients. Therefore, studying frontal cortex changes is of great importance for AD research. This paper aims to segment frontal cortex from brain PET imaging using deep neural networks. The learning framework called Frontal cortex Segmentation model of brain PET imaging (FSPET) is proposed to tackle this problem. It combines the anatomical prior to frontal cortex into the segmentation model, which is based on conditional generative adversarial network and convolutional auto-encoder. The FSPET method is evaluated on a dataset of 30 brain PET imaging with ground truth annotated by a radiologist. Results that outperform other baselines demonstrate the effectiveness of the FSPET framework.
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Affiliation(s)
- Qianyi Zhan
- School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China.,Jiangsu Key Laboratory of Media Design and Software Technology, Wuxi, China
| | - Yuanyuan Liu
- School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China.,Jiangsu Key Laboratory of Media Design and Software Technology, Wuxi, China
| | - Yuan Liu
- School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, China.,Jiangsu Key Laboratory of Media Design and Software Technology, Wuxi, China
| | - Wei Hu
- Department of Nuclear Medicine, Nanjing Medical University, Affiliated Wuxi People's Hospital, Wuxi, China
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46
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Ganesh SS, Kannayeram G, Karthick A, Muhibbullah M. A Novel Context Aware Joint Segmentation and Classification Framework for Glaucoma Detection. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2021; 2021:2921737. [PMID: 34777561 PMCID: PMC8589492 DOI: 10.1155/2021/2921737] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/19/2021] [Revised: 10/19/2021] [Accepted: 10/23/2021] [Indexed: 11/17/2022]
Abstract
Glaucoma is a chronic ocular disease characterized by damage to the optic nerve resulting in progressive and irreversible visual loss. Early detection and timely clinical interventions are critical in improving glaucoma-related outcomes. As a typical and complicated ocular disease, glaucoma detection presents a unique challenge due to its insidious onset and high intra- and interpatient variabilities. Recent studies have demonstrated that robust glaucoma detection systems can be realized with deep learning approaches. The optic disc (OD) is the most commonly studied retinal structure for screening and diagnosing glaucoma. This paper proposes a novel context aware deep learning framework called GD-YNet, for OD segmentation and glaucoma detection. It leverages the potential of aggregated transformations and the simplicity of the YNet architecture in context aware OD segmentation and binary classification for glaucoma detection. Trained with the RIGA and RIMOne-V2 datasets, this model achieves glaucoma detection accuracies of 99.72%, 98.02%, 99.50%, and 99.41% with the ACRIMA, Drishti-gs, REFUGE, and RIMOne-V1 datasets. Further, the proposed model can be extended to a multiclass segmentation and classification model for glaucoma staging and severity assessment.
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Affiliation(s)
- S. Sankar Ganesh
- Department of Artificial Intelligence and Data Science, KPR Institute of Engineering and Technology, Coimbatore, 641407 Tamil Nadu, India
| | - G. Kannayeram
- Department of Electrical and Electronics Engineering, National Engineering College, Kovilpatti, 628503 Tamil Nadu, India
| | - Alagar Karthick
- Renewable Energy Lab, Department of Electrical and Electronics Engineering, KPR Institute of Engineering and Technology, Coimbatore, 641407 Tamil Nadu, India
| | - M. Muhibbullah
- Department of Electrical and Electronic Engineering, Bangladesh University, Dhaka 1207, Bangladesh
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47
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Shekar S, Satpute N, Gupta A. Review on diabetic retinopathy with deep learning methods. JOURNAL OF MEDICAL IMAGING (BELLINGHAM, WASH.) 2021; 8:060901. [PMID: 34859116 DOI: 10.1117/1.jmi.8.6.060901] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Accepted: 10/27/2021] [Indexed: 11/14/2022]
Abstract
Purpose: The purpose of our review paper is to examine many existing works of literature presenting the different methods utilized for diabetic retinopathy (DR) recognition employing deep learning (DL) and machine learning (ML) techniques, and also to address the difficulties faced in various datasets used by DR. Approach: DR is a progressive illness and may become a reason for vision loss. Early identification of DR lesions is, therefore, helpful and prevents damage to the retina. However, it is a complex job in view of the fact that it is symptomless earlier, and also ophthalmologists have been needed in traditional approaches. Recently, automated identification of DR-based studies has been stated based on image processing, ML, and DL. We analyze the recent literature and provide a comparative study that also includes the limitations of the literature and future work directions. Results: A relative analysis among the databases used, performance metrics employed, and ML and DL techniques adopted recently in DR detection based on various DR features is presented. Conclusion: Our review paper discusses the methods employed in DR detection along with the technical and clinical challenges that are encountered, which is missing in existing reviews, as well as future scopes to assist researchers in the field of retinal imaging.
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Affiliation(s)
- Shreya Shekar
- College of Engineering Pune, Department of Electronics and Telecommunication Engineering, Pune, Maharashtra, India
| | - Nitin Satpute
- Aarhus University, Department of Electrical and Computer Engineering, Aarhus, Denmark
| | - Aditya Gupta
- College of Engineering Pune, Department of Electronics and Telecommunication Engineering, Pune, Maharashtra, India
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48
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Hao Y, Xie H, Qiu R. Construction and application of color fundus image segmentation algorithm based on Multi-Scale local combined global enhancement. Pak J Med Sci 2021; 37:1595-1599. [PMID: 34712289 PMCID: PMC8520383 DOI: 10.12669/pjms.37.6-wit.4848] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2021] [Revised: 06/08/2021] [Accepted: 07/05/2021] [Indexed: 11/15/2022] Open
Abstract
Objective Aiming at the problem of low accuracy in extracting small blood vessels from existing retinal blood vessel images, a retinal blood vessel segmentation method based on a combination of a multi-scale linear detector and local and global enhancement is proposed. Methods The multi-scale line detector is studied, and it is divided into two parts: small scale and large scale. The small scale is used to detect the locally enhanced image and the large scale is used to detect the globally enhanced image. Fusion the response functions at different scales to get the final retinal vascular structure. Results Experiments on two databases STARE and DRIVE, show that the average vascular accuracy rates obtained by the algorithm reach 96.62% and 96.45%, and the average true positive rates reach 75.52% and 83.07%, respectively. Conclusion The segmentation accuracy is high, and better blood vessel segmentation results can be obtained.
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Affiliation(s)
- Yanjie Hao
- Yanjie Hao, Associate Chief Physician, Department of Ophthalmology, Jiaozhou Central Hospital of Qingdao, Qingdao, 266300, Shandong, China
| | - Hongbo Xie
- Hongbo Xie, Attending Physician, Department of Ophthalmology, Qingdao Women's and Children's Hospital, Qingdao, 266000, Shandong, China
| | - Rong Qiu
- Rong Qiu, Associate Chief Physician, Department of Ophthalmology, Zhucheng People's Hospital, Zhucheng, Shandong, China
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Pachade S, Porwal P, Kokare M, Giancardo L, Mériaudeau F. NENet: Nested EfficientNet and adversarial learning for joint optic disc and cup segmentation. Med Image Anal 2021; 74:102253. [PMID: 34614474 DOI: 10.1016/j.media.2021.102253] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2020] [Revised: 03/15/2021] [Accepted: 09/22/2021] [Indexed: 01/27/2023]
Abstract
Glaucoma is an ocular disease threatening irreversible vision loss. Primary screening of Glaucoma involves computation of optic cup (OC) to optic disc (OD) ratio that is widely accepted metric. Recent deep learning frameworks for OD and OC segmentation have shown promising results and ways to attain remarkable performance. In this paper, we present a novel segmentation network, Nested EfficientNet (NENet) that consists of EfficientNetB4 as an encoder along with a nested network of pre-activated residual blocks, atrous spatial pyramid pooling (ASPP) block and attention gates (AGs). The combination of cross-entropy and dice coefficient (DC) loss is utilized to guide the network for accurate segmentation. Further, a modified patch-based discriminator is designed for use with the NENet to improve the local segmentation details. Three publicly available datasets, REFUGE, Drishti-GS, and RIM-ONE-r3 were utilized to evaluate the performances of the proposed network. In our experiments, NENet outperformed state-of-the-art methods for segmentation of OD and OC. Additionally, we show that NENet has excellent generalizability across camera types and image resolution. The obtained results suggest that the proposed technique has potential to be an important component for an automated Glaucoma screening system.
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Affiliation(s)
- Samiksha Pachade
- Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded, India.
| | - Prasanna Porwal
- Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded, India
| | - Manesh Kokare
- Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded, India
| | - Luca Giancardo
- School of Biomedical Informatics, The University of Texas Health Science Center at Houston, USA
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50
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Ding L, Kuriyan AE, Ramchandran RS, Wykoff CC, Sharma G. Weakly-Supervised Vessel Detection in Ultra-Widefield Fundus Photography via Iterative Multi-Modal Registration and Learning. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:2748-2758. [PMID: 32991281 PMCID: PMC8513803 DOI: 10.1109/tmi.2020.3027665] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
We propose a deep-learning based annotation-efficient framework for vessel detection in ultra-widefield (UWF) fundus photography (FP) that does not require de novo labeled UWF FP vessel maps. Our approach utilizes concurrently captured UWF fluorescein angiography (FA) images, for which effective deep learning approaches have recently become available, and iterates between a multi-modal registration step and a weakly-supervised learning step. In the registration step, the UWF FA vessel maps detected with a pre-trained deep neural network (DNN) are registered with the UWF FP via parametric chamfer alignment. The warped vessel maps can be used as the tentative training data but inevitably contain incorrect (noisy) labels due to the differences between FA and FP modalities and the errors in the registration. In the learning step, a robust learning method is proposed to train DNNs with noisy labels. The detected FP vessel maps are used for the registration in the following iteration. The registration and the vessel detection benefit from each other and are progressively improved. Once trained, the UWF FP vessel detection DNN from the proposed approach allows FP vessel detection without requiring concurrently captured UWF FA images. We validate the proposed framework on a new UWF FP dataset, PRIME-FP20, and on existing narrow-field FP datasets. Experimental evaluation, using both pixel-wise metrics and the CAL metrics designed to provide better agreement with human assessment, shows that the proposed approach provides accurate vessel detection, without requiring manually labeled UWF FP training data.
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