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Lakshminarayanan V, Kheradfallah H, Sarkar A, Jothi Balaji J. Automated Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Survey. J Imaging 2021; 7:165. [PMID: 34460801 PMCID: PMC8468161 DOI: 10.3390/jimaging7090165] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 08/23/2021] [Accepted: 08/24/2021] [Indexed: 12/16/2022] Open
Abstract
Diabetic Retinopathy (DR) is a leading cause of vision loss in the world. In the past few years, artificial intelligence (AI) based approaches have been used to detect and grade DR. Early detection enables appropriate treatment and thus prevents vision loss. For this purpose, both fundus and optical coherence tomography (OCT) images are used to image the retina. Next, Deep-learning (DL)-/machine-learning (ML)-based approaches make it possible to extract features from the images and to detect the presence of DR, grade its severity and segment associated lesions. This review covers the literature dealing with AI approaches to DR such as ML and DL in classification and segmentation that have been published in the open literature within six years (2016-2021). In addition, a comprehensive list of available DR datasets is reported. This list was constructed using both the PICO (P-Patient, I-Intervention, C-Control, O-Outcome) and Preferred Reporting Items for Systematic Review and Meta-analysis (PRISMA) 2009 search strategies. We summarize a total of 114 published articles which conformed to the scope of the review. In addition, a list of 43 major datasets is presented.
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Affiliation(s)
- Vasudevan Lakshminarayanan
- Theoretical and Experimental Epistemology Lab, School of Optometry and Vision Science, University of Waterloo, Waterloo, ON N2L 3G1, Canada;
| | - Hoda Kheradfallah
- Theoretical and Experimental Epistemology Lab, School of Optometry and Vision Science, University of Waterloo, Waterloo, ON N2L 3G1, Canada;
| | - Arya Sarkar
- Department of Computer Engineering, University of Engineering and Management, Kolkata 700 156, India;
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Li M, Chen Y, Ji Z, Xie K, Yuan S, Chen Q, Li S. Image Projection Network: 3D to 2D Image Segmentation in OCTA Images. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:3343-3354. [PMID: 32365023 DOI: 10.1109/tmi.2020.2992244] [Citation(s) in RCA: 45] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
We present an image projection network (IPN), which is a novel end-to-end architecture and can achieve 3D-to-2D image segmentation in optical coherence tomography angiography (OCTA) images. Our key insight is to build a projection learning module (PLM) which uses a unidirectional pooling layer to conduct effective features selection and dimension reduction concurrently. By combining multiple PLMs, the proposed network can input 3D OCTA data, and output 2D segmentation results such as retinal vessel segmentation. It provides a new idea for the quantification of retinal indicators: without retinal layer segmentation and without projection maps. We tested the performance of our network for two crucial retinal image segmentation issues: retinal vessel (RV) segmentation and foveal avascular zone (FAZ) segmentation. The experimental results on 316 OCTA volumes demonstrate that the IPN is an effective implementation of 3D-to-2D segmentation networks, and the uses of multi-modality information and volumetric information make IPN perform better than the baseline methods.
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Cheimariotis GA, Chatzizisis YS, Koutkias VG, Toutouzas K, Giannopoulos A, Riga M, Chouvarda I, Antoniadis AP, Doulaverakis C, Tsamboulatidis I, Kompatsiaris I, Giannoglou GD, Maglaveras N. ARCOCT: Automatic detection of lumen border in intravascular OCT images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017; 151:21-32. [PMID: 28947003 DOI: 10.1016/j.cmpb.2017.08.007] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/04/2016] [Revised: 07/29/2017] [Accepted: 08/08/2017] [Indexed: 06/07/2023]
Abstract
BACKGROUND AND OBJECTIVE Intravascular optical coherence tomography (OCT) is an invaluable tool for the detection of pathological features on the arterial wall and the investigation of post-stenting complications. Computational lumen border detection in OCT images is highly advantageous, since it may support rapid morphometric analysis. However, automatic detection is very challenging, since OCT images typically include various artifacts that impact image clarity, including features such as side branches and intraluminal blood presence. This paper presents ARCOCT, a segmentation method for fully-automatic detection of lumen border in OCT images. METHODS ARCOCT relies on multiple, consecutive processing steps, accounting for image preparation, contour extraction and refinement. In particular, for contour extraction ARCOCT employs the transformation of OCT images based on physical characteristics such as reflectivity and absorption of the tissue and, for contour refinement, local regression using weighted linear least squares and a 2nd degree polynomial model is employed to achieve artifact and small-branch correction as well as smoothness of the artery mesh. Our major focus was to achieve accurate contour delineation in the various types of OCT images, i.e., even in challenging cases with branches and artifacts. RESULTS ARCOCT has been assessed in a dataset of 1812 images (308 from stented and 1504 from native segments) obtained from 20 patients. ARCOCT was compared against ground-truth manual segmentation performed by experts on the basis of various geometric features (e.g. area, perimeter, radius, diameter, centroid, etc.) and closed contour matching indicators (the Dice index, the Hausdorff distance and the undirected average distance), using standard statistical analysis methods. The proposed method was proven very efficient and close to the ground-truth, exhibiting non statistically-significant differences for most of the examined metrics. CONCLUSIONS ARCOCT allows accurate and fully-automated lumen border detection in OCT images.
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Affiliation(s)
- Grigorios-Aris Cheimariotis
- Lab of Computing Medical Informatics and Biomedical Imaging Technologies, Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece; Institute of Applied Biosciences, Center for Research and Technology - Hellas, Thermi, Thessaloniki, Greece
| | - Yiannis S Chatzizisis
- Cardiovascular Division, University of Nebraska Medical Center, Omaha, Nebraska, USA
| | - Vassilis G Koutkias
- Institute of Applied Biosciences, Center for Research and Technology - Hellas, Thermi, Thessaloniki, Greece; Lab of Computing Medical Informatics and Biomedical Imaging Technologies, Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Konstantinos Toutouzas
- 1st Department of Cardiology, Athens Medical School, Hippokration Hospital, Athens, Greece
| | - Andreas Giannopoulos
- Applied Imaging Science Lab, Radiology Department, Brigham and Women's Hospital, Harvard Medical School, Massachusetts, USA
| | - Maria Riga
- 1st Department of Cardiology, Athens Medical School, Hippokration Hospital, Athens, Greece
| | - Ioanna Chouvarda
- Lab of Computing Medical Informatics and Biomedical Imaging Technologies, Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece; Institute of Applied Biosciences, Center for Research and Technology - Hellas, Thermi, Thessaloniki, Greece
| | - Antonios P Antoniadis
- Cardiovascular Division, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Charalambos Doulaverakis
- Information Technologies Institute, Center for Research and Technology - Hellas, Thermi, Thessaloniki, Greece
| | - Ioannis Tsamboulatidis
- Information Technologies Institute, Center for Research and Technology - Hellas, Thermi, Thessaloniki, Greece
| | - Ioannis Kompatsiaris
- Information Technologies Institute, Center for Research and Technology - Hellas, Thermi, Thessaloniki, Greece
| | - George D Giannoglou
- 1st Department of Cardiology, AHEPA University Hospital, Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Nicos Maglaveras
- Lab of Computing Medical Informatics and Biomedical Imaging Technologies, Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece; Institute of Applied Biosciences, Center for Research and Technology - Hellas, Thermi, Thessaloniki, Greece.
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Ahdi A, Rabbani H, Vard A. A hybrid method for 3D mosaicing of OCT images of macula and Optic Nerve Head. Comput Biol Med 2017; 91:277-290. [PMID: 29102825 DOI: 10.1016/j.compbiomed.2017.10.031] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2017] [Revised: 10/26/2017] [Accepted: 10/26/2017] [Indexed: 10/18/2022]
Abstract
A mosaiced image is the result of merging two or more images with overlapping area in order to generate a high resolution panorama of a large scene. A wide view of Optical Coherence Tomography (OCT) images can help clinicians in diagnosis by enabling simultaneous analysis of different portions of the gathered information. In this paper, we present a novel method for mosaicing of 3D OCT images of macula and Optic Nerve Head (ONH) that is carried out in two phases; registration of OCT projections and mosaicing of B-scans. In the first phase, in order to register the OCT projection images of macula and ONH, their corresponding color fundus image is considered as the main frame and the geometrical features of their curvelet-based extracted vessels are employed for registration. The registration parameters obtained are then applied on all x-y slices of the 3D OCT images of macula and ONH. In the B-scan mosaicing phase, the overlapping areas of corresponding reprojected B-scans are extracted and the best registration model is obtained based on line-by-line matching of corresponding A-scans in overlapping areas. This registration model is then applied to the remaining A-scans of the ONH-based B-scan. The aligned B-scans of macular OCT and OCT images of ONH are finally blended and 3D mosaiced OCT images are obtained. Two criteria are considered for assessment of mosaiced images; the quality of alignment/mosaicing of B-scans and the loss of clinical information from the B-scans after mosaicing. The average grading values of 3.5 ± 0.74 and 3.63 ± 0.55 (out of 4) are obtained for the first and second criteria, respectively.
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Affiliation(s)
- Alieh Ahdi
- Dept. of Biomedical Engineering, School of Advanced Technologies in Medicine, Medical Image & Signal Processing Research Center, Isfahan University of Medical Sciences, Iran
| | - Hossein Rabbani
- Dept. of Biomedical Engineering, School of Advanced Technologies in Medicine, Medical Image & Signal Processing Research Center, Isfahan University of Medical Sciences, Iran.
| | - Alireza Vard
- Dept. of Biomedical Engineering, School of Advanced Technologies in Medicine, Medical Image & Signal Processing Research Center, Isfahan University of Medical Sciences, Iran
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Automatic blood vessels segmentation based on different retinal maps from OCTA scans. Comput Biol Med 2017; 89:150-161. [PMID: 28806613 DOI: 10.1016/j.compbiomed.2017.08.008] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2017] [Revised: 08/03/2017] [Accepted: 08/04/2017] [Indexed: 11/23/2022]
Abstract
The retinal vascular network reflects the health of the retina, which is a useful diagnostic indicator of systemic vascular. Therefore, the segmentation of retinal blood vessels is a powerful method for diagnosing vascular diseases. This paper presents an automatic segmentation system for retinal blood vessels from Optical Coherence Tomography Angiography (OCTA) images. The system segments blood vessels from the superficial and deep retinal maps for normal and diabetic cases. Initially, we reduced the noise and improved the contrast of the OCTA images by using the Generalized Gauss-Markov random field (GGMRF) model. Secondly, we proposed a joint Markov-Gibbs random field (MGRF) model to segment the retinal blood vessels from other background tissues. It integrates both appearance and spatial models in addition to the prior probability model of OCTA images. The higher order MGRF (HO-MGRF) model in addition to the 1st-order intensity model are used to consider the spatial information in order to overcome the low contrast between vessels and other tissues. Finally, we refined the segmentation by extracting connected regions using a 2D connectivity filter. The proposed segmentation system was trained and tested on 47 data sets, which are 23 normal data sets and 24 data sets for diabetic patients. To evaluate the accuracy and robustness of the proposed segmentation framework, we used three different metrics, which are Dice similarity coefficient (DSC), absolute vessels volume difference (VVD), and area under the curve (AUC). The results on OCTA data sets (DSC=95.04±3.75%, VVD=8.51±1.49%, and AUC=95.20±1.52%) show the promise of the proposed segmentation approach.
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Fang L, Li S, Cunefare D, Farsiu S. Segmentation Based Sparse Reconstruction of Optical Coherence Tomography Images. IEEE TRANSACTIONS ON MEDICAL IMAGING 2017; 36:407-421. [PMID: 27662673 PMCID: PMC5363080 DOI: 10.1109/tmi.2016.2611503] [Citation(s) in RCA: 38] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
We demonstrate the usefulness of utilizing a segmentation step for improving the performance of sparsity based image reconstruction algorithms. In specific, we will focus on retinal optical coherence tomography (OCT) reconstruction and propose a novel segmentation based reconstruction framework with sparse representation, termed segmentation based sparse reconstruction (SSR). The SSR method uses automatically segmented retinal layer information to construct layer-specific structural dictionaries. In addition, the SSR method efficiently exploits patch similarities within each segmented layer to enhance the reconstruction performance. Our experimental results on clinical-grade retinal OCT images demonstrate the effectiveness and efficiency of the proposed SSR method for both denoising and interpolation of OCT images.
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