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Rasti R, Mehridehnavi A, Rabbani H, Hajizadeh F. Automatic diagnosis of abnormal macula in retinal optical coherence tomography images using wavelet-based convolutional neural network features and random forests classifier. JOURNAL OF BIOMEDICAL OPTICS 2018; 23:1-10. [PMID: 29564864 DOI: 10.1117/1.jbo.23.3.035005] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2017] [Accepted: 02/27/2018] [Indexed: 05/27/2023]
Abstract
The present research intends to propose a fully automatic algorithm for the classification of three-dimensional (3-D) optical coherence tomography (OCT) scans of patients suffering from abnormal macula from normal candidates. The method proposed does not require any denoising, segmentation, retinal alignment processes to assess the intraretinal layers, as well as abnormalities or lesion structures. To classify abnormal cases from the control group, a two-stage scheme was utilized, which consists of automatic subsystems for adaptive feature learning and diagnostic scoring. In the first stage, a wavelet-based convolutional neural network (CNN) model was introduced and exploited to generate B-scan representative CNN codes in the spatial-frequency domain, and the cumulative features of 3-D volumes were extracted. In the second stage, the presence of abnormalities in 3-D OCTs was scored over the extracted features. Two different retinal SD-OCT datasets are used for evaluation of the algorithm based on the unbiased fivefold cross-validation (CV) approach. The first set constitutes 3-D OCT images of 30 normal subjects and 30 diabetic macular edema (DME) patients captured from the Topcon device. The second publicly available set consists of 45 subjects with a distribution of 15 patients in age-related macular degeneration, DME, and normal classes from the Heidelberg device. With the application of the algorithm on overall OCT volumes and 10 repetitions of the fivefold CV, the proposed scheme obtained an average precision of 99.33% on dataset1 as a two-class classification problem and 98.67% on dataset2 as a three-class classification task.
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Affiliation(s)
- Reza Rasti
- Isfahan University of Medical Sciences, School of Advanced Technologies in Medicine, Isfahan Departm, Iran
- Isfahan University of Medical Sciences, Medical Image and Signal Processing Research Center, Isfahan, Iran
| | - Alireza Mehridehnavi
- Isfahan University of Medical Sciences, School of Advanced Technologies in Medicine, Isfahan Departm, Iran
- Isfahan University of Medical Sciences, Medical Image and Signal Processing Research Center, Isfahan, Iran
| | - Hossein Rabbani
- Isfahan University of Medical Sciences, School of Advanced Technologies in Medicine, Isfahan Departm, Iran
- Isfahan University of Medical Sciences, Medical Image and Signal Processing Research Center, Isfahan, Iran
| | - Fedra Hajizadeh
- Noor Eye Hospital, Noor Ophthalmology Research Center, Tehran, Iran
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152
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Rembold D, Kromer R, Wagenfeld L, Grigat RR. An Automated Approach for Inner Segment/Outer Segment Defect Detection in Retinal SD-OCT Images. J Med Biol Eng 2018. [DOI: 10.1007/s40846-018-0377-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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153
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Fang L, Wang C, Li S, Yan J, Chen X, Rabbani H. Automatic classification of retinal three-dimensional optical coherence tomography images using principal component analysis network with composite kernels. JOURNAL OF BIOMEDICAL OPTICS 2017; 22:1-10. [PMID: 29188661 DOI: 10.1117/1.jbo.22.11.116011] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/10/2017] [Accepted: 11/08/2017] [Indexed: 05/07/2023]
Abstract
We present an automatic method, termed as the principal component analysis network with composite kernel (PCANet-CK), for the classification of three-dimensional (3-D) retinal optical coherence tomography (OCT) images. Specifically, the proposed PCANet-CK method first utilizes the PCANet to automatically learn features from each B-scan of the 3-D retinal OCT images. Then, multiple kernels are separately applied to a set of very important features of the B-scans and these kernels are fused together, which can jointly exploit the correlations among features of the 3-D OCT images. Finally, the fused (composite) kernel is incorporated into an extreme learning machine for the OCT image classification. We tested our proposed algorithm on two real 3-D spectral domain OCT (SD-OCT) datasets (of normal subjects and subjects with the macular edema and age-related macular degeneration), which demonstrated its effectiveness.
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Affiliation(s)
- Leyuan Fang
- Hunan University, College of Electrical and Information Engineering, Changsha, China
| | - Chong Wang
- Hunan University, College of Electrical and Information Engineering, Changsha, China
| | - Shutao Li
- Hunan University, College of Electrical and Information Engineering, Changsha, China
| | - Jun Yan
- Hunan University, College of Electrical and Information Engineering, Changsha, China
| | - Xiangdong Chen
- The First Affiliated Hospital of Hunan University of Chinese Medicine, Department of Ophthalmology,, China
| | - Hossein Rabbani
- Isfahan University of Medical Sciences, Medical Image and Signal Processing Research Center, Isfahan, Iran
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154
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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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155
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McNabb RP, Tian J, Farsiu S, Izatt JA, Lad EM, Kuo AN. Retinal imaging in human autopsy eyes using a custom optical coherence tomography periscope. BIOMEDICAL OPTICS EXPRESS 2017; 8:4152-4159. [PMID: 28966854 PMCID: PMC5611930 DOI: 10.1364/boe.8.004152] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2017] [Revised: 08/09/2017] [Accepted: 08/09/2017] [Indexed: 06/07/2023]
Abstract
Age-related macular degeneration (AMD) is a major cause of vision loss in the elderly. To better study the pathobiology of AMD, postmortem eyes offer an excellent opportunity to correlate optical coherence tomography (OCT) imaging characteristics with histopathology. However, postmortem eyes from autopsy present challenges to standard OCT imaging including opaque anterior segment structures and standard of care autopsy processing resulting in oblique views to the macula. To overcome these challenges, we report a custom periscope attached by a standard mount to an OCT sample arm and demonstrate high quality macular OCT acquisitions in autopsy-processed eyes.
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Affiliation(s)
- Ryan P. McNabb
- Department of Ophthalmology, Duke University Medical Center, 2351 Erwin Rd, Durham, NC 27705, USA
| | - James Tian
- Department of Ophthalmology, Duke University Medical Center, 2351 Erwin Rd, Durham, NC 27705, USA
| | - Sina Farsiu
- Department of Ophthalmology, Duke University Medical Center, 2351 Erwin Rd, Durham, NC 27705, USA
- Department of Biomedical Engineering, Duke University, 101 Science Drive, Durham, NC 27708, USA
| | - Joseph A. Izatt
- Department of Ophthalmology, Duke University Medical Center, 2351 Erwin Rd, Durham, NC 27705, USA
- Department of Biomedical Engineering, Duke University, 101 Science Drive, Durham, NC 27708, USA
| | - Eleonora M. Lad
- Department of Ophthalmology, Duke University Medical Center, 2351 Erwin Rd, Durham, NC 27705, USA
| | - Anthony N. Kuo
- Department of Ophthalmology, Duke University Medical Center, 2351 Erwin Rd, Durham, NC 27705, USA
- Department of Biomedical Engineering, Duke University, 101 Science Drive, Durham, NC 27708, USA
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156
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Wintergerst MWM, Schultz T, Birtel J, Schuster AK, Pfeiffer N, Schmitz-Valckenberg S, Holz FG, Finger RP. Algorithms for the Automated Analysis of Age-Related Macular Degeneration Biomarkers on Optical Coherence Tomography: A Systematic Review. Transl Vis Sci Technol 2017; 6:10. [PMID: 28729948 PMCID: PMC5516568 DOI: 10.1167/tvst.6.4.10] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2017] [Accepted: 05/30/2017] [Indexed: 11/24/2022] Open
Abstract
PURPOSE To assess the quality of optical coherence tomography (OCT) grading algorithms for retinal biomarkers of age-related macular degeneration (AMD). METHODS Following a systematic review of the literature data on detection and quantification of AMD retinal biomarkers by available algorithms were extracted and descriptively synthesized. Algorithm quality was assessed using a modified version of the Quality Assessment of Diagnostic Accuracy Studies 2 checklist with a focus on accuracy against established reference standards and risk of bias. RESULTS Thirty five studies reporting computer-aided diagnosis (CAD) tools for qualitative analysis or algorithms for quantitative analysis were identified. Compared with manual assessment in reference standards correlation coefficients ranged from 0.54 to 0.97 for drusen, 0.80 to 0.98 for geographic atrophy (GA), and 0.30 to 0.98 for intra- or subretinal fluid and pigment epithelial detachment (PED) detection by automated algorithms. CAD tools achieved area under the curve (AUC) values of 0.94 to 0.99, sensitivity of 0.90 to 1.00, and specificity of 0.89 to 0.92. CONCLUSIONS Automated analysis of AMD biomarkers on OCT is promising. However, most of the algorithm validation was performed in preselected patients, exhibiting the targeted biomarker only. In addition, type and quality of reported algorithm validation varied substantially. TRANSLATIONAL RELEVANCE The development of algorithms for combined, simultaneous analysis of multiple AMD biomarkers including AMD staging and the agreement on standardized validation procedures would be of considerable translational value for the clinician and the clinical researcher.
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Affiliation(s)
| | - Thomas Schultz
- Department of Computer Science, University of Bonn, Bonn, Germany
| | - Johannes Birtel
- Department of Ophthalmology, University of Bonn, Bonn, Germany
| | | | - Norbert Pfeiffer
- Department of Ophthalmology, University Medical Center Mainz, Mainz, Germany
| | | | - Frank G Holz
- Department of Ophthalmology, University of Bonn, Bonn, Germany
| | - Robert P Finger
- Department of Ophthalmology, University of Bonn, Bonn, Germany
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157
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Alsaih K, Lemaitre G, Rastgoo M, Massich J, Sidibé D, Meriaudeau F. Machine learning techniques for diabetic macular edema (DME) classification on SD-OCT images. Biomed Eng Online 2017; 16:68. [PMID: 28592309 PMCID: PMC5463338 DOI: 10.1186/s12938-017-0352-9] [Citation(s) in RCA: 51] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2016] [Accepted: 05/16/2017] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND Spectral domain optical coherence tomography (OCT) (SD-OCT) is most widely imaging equipment used in ophthalmology to detect diabetic macular edema (DME). Indeed, it offers an accurate visualization of the morphology of the retina as well as the retina layers. METHODS The dataset used in this study has been acquired by the Singapore Eye Research Institute (SERI), using CIRRUS TM (Carl Zeiss Meditec, Inc., Dublin, CA, USA) SD-OCT device. The dataset consists of 32 OCT volumes (16 DME and 16 normal cases). Each volume contains 128 B-scans with resolution of 1024 px × 512 px, resulting in more than 3800 images being processed. All SD-OCT volumes are read and assessed by trained graders and identified as normal or DME cases based on evaluation of retinal thickening, hard exudates, intraretinal cystoid space formation, and subretinal fluid. Within the DME sub-set, a large number of lesions has been selected to create a rather complete and diverse DME dataset. This paper presents an automatic classification framework for SD-OCT volumes in order to identify DME versus normal volumes. In this regard, a generic pipeline including pre-processing, feature detection, feature representation, and classification was investigated. More precisely, extraction of histogram of oriented gradients and local binary pattern (LBP) features within a multiresolution approach is used as well as principal component analysis (PCA) and bag of words (BoW) representations. RESULTS AND CONCLUSION Besides comparing individual and combined features, different representation approaches and different classifiers are evaluated. The best results are obtained for LBP[Formula: see text] vectors while represented and classified using PCA and a linear-support vector machine (SVM), leading to a sensitivity(SE) and specificity (SP) of 87.5 and 87.5%, respectively.
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Affiliation(s)
- Khaled Alsaih
- LE2I, CNRS, Arts et Métiers, Université Bourgogne Franche-Comté, 12 rue de la Fonderie, Le Creusot, France
- Centre for Intelligent Signal and Imaging Research (CISIR), Electrical & Electronic Engineering Department, Universiti Teknologi PETRONAS, 32610 Seri Iskandar, Malaysia
| | - Guillaume Lemaitre
- LE2I, CNRS, Arts et Métiers, Université Bourgogne Franche-Comté, 12 rue de la Fonderie, Le Creusot, France
| | - Mojdeh Rastgoo
- LE2I, CNRS, Arts et Métiers, Université Bourgogne Franche-Comté, 12 rue de la Fonderie, Le Creusot, France
| | - Joan Massich
- LE2I, CNRS, Arts et Métiers, Université Bourgogne Franche-Comté, 12 rue de la Fonderie, Le Creusot, France
| | - Désiré Sidibé
- LE2I, CNRS, Arts et Métiers, Université Bourgogne Franche-Comté, 12 rue de la Fonderie, Le Creusot, France
| | - Fabrice Meriaudeau
- LE2I, CNRS, Arts et Métiers, Université Bourgogne Franche-Comté, 12 rue de la Fonderie, Le Creusot, France
- Centre for Intelligent Signal and Imaging Research (CISIR), Electrical & Electronic Engineering Department, Universiti Teknologi PETRONAS, 32610 Seri Iskandar, Malaysia
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158
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Fang L, Yang L, Li S, Rabbani H, Liu Z, Peng Q, Chen X. Automatic detection and recognition of multiple macular lesions in retinal optical coherence tomography images with multi-instance multilabel learning. JOURNAL OF BIOMEDICAL OPTICS 2017; 22:66014. [PMID: 28655052 DOI: 10.1117/1.jbo.22.6.066014] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2017] [Accepted: 06/02/2017] [Indexed: 06/07/2023]
Abstract
Detection and recognition of macular lesions in optical coherence tomography (OCT) are very important for retinal diseases diagnosis and treatment. As one kind of retinal disease (e.g., diabetic retinopathy) may contain multiple lesions (e.g., edema, exudates, and microaneurysms) and eye patients may suffer from multiple retinal diseases, multiple lesions often coexist within one retinal image. Therefore, one single-lesion-based detector may not support the diagnosis of clinical eye diseases. To address this issue, we propose a multi-instance multilabel-based lesions recognition (MIML-LR) method for the simultaneous detection and recognition of multiple lesions. The proposed MIML-LR method consists of the following steps: (1) segment the regions of interest (ROIs) for different lesions, (2) compute descriptive instances (features) for each lesion region, (3) construct multilabel detectors, and (4) recognize each ROI with the detectors. The proposed MIML-LR method was tested on 823 clinically labeled OCT images with normal macular and macular with three common lesions: epiretinal membrane, edema, and drusen. For each input OCT image, our MIML-LR method can automatically identify the number of lesions and assign the class labels, achieving the average accuracy of 88.72% for the cases with multiple lesions, which better assists macular disease diagnosis and treatment.
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Affiliation(s)
- Leyuan Fang
- Hunan University, College of Electrical and Information Engineering, Changsha, Hunan, China
| | - Liumao Yang
- Hunan University, College of Electrical and Information Engineering, Changsha, Hunan, China
| | - Shutao Li
- Hunan University, College of Electrical and Information Engineering, Changsha, Hunan, China
| | - Hossein Rabbani
- Isfahan University of Medical Sciences, Medical Image and Signal Processing Research Center, Isfahan, Iran
| | - Zhimin Liu
- The First Affiliated Hospital of Hunan University of Chinese Medicine, Department of Ophthalmology, Changsha, Hunan, China
| | - Qinghua Peng
- The First Affiliated Hospital of Hunan University of Chinese Medicine, Department of Ophthalmology, Changsha, Hunan, China
| | - Xiangdong Chen
- The First Affiliated Hospital of Hunan University of Chinese Medicine, Department of Ophthalmology, Changsha, Hunan, China
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159
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Novosel J, Vermeer KA, de Jong JH, van Vliet LJ. Joint Segmentation of Retinal Layers and Focal Lesions in 3-D OCT Data of Topologically Disrupted Retinas. IEEE TRANSACTIONS ON MEDICAL IMAGING 2017; 36:1276-1286. [PMID: 28186886 DOI: 10.1109/tmi.2017.2666045] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Accurate quantification of retinal structures in 3-D optical coherence tomography data of eyes with pathologies provides clinically relevant information. We present an approach to jointly segment retinal layers and lesions in eyes with topology-disrupting retinal diseases by a loosely coupled level set framework. In the new approach, lesions are modeled as an additional space-variant layer delineated by auxiliary interfaces. Furthermore, the segmentation of interfaces is steered by local differences in the signal between adjacent retinal layers, thereby allowing the approach to handle local intensity variations. The accuracy of the proposed method of both layer and lesion segmentation has been evaluated on eyes affected by central serous retinopathy and age-related macular degeneration. In addition, layer segmentation of the proposed approach was evaluated on eyes without topology-disrupting retinal diseases. Good agreement between the segmentation performed manually by a medical doctor and results obtained from the automatic segmentation was found for all data types. The mean unsigned error for all interfaces varied between 2.3 and 11.9 μm (0.6-3.1 pixels). Furthermore, lesion segmentation showed a Dice coefficient of 0.68 for drusen and 0.89 for fluid pockets. Overall, the method provides a flexible and accurate solution to jointly segment lesions and retinal layers.
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160
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Fang L, Cunefare D, Wang C, Guymer RH, Li S, Farsiu S. Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search. BIOMEDICAL OPTICS EXPRESS 2017; 8:2732-2744. [PMID: 28663902 PMCID: PMC5480509 DOI: 10.1364/boe.8.002732] [Citation(s) in RCA: 262] [Impact Index Per Article: 37.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/14/2017] [Revised: 04/22/2017] [Accepted: 04/23/2017] [Indexed: 05/18/2023]
Abstract
We present a novel framework combining convolutional neural networks (CNN) and graph search methods (termed as CNN-GS) for the automatic segmentation of nine layer boundaries on retinal optical coherence tomography (OCT) images. CNN-GS first utilizes a CNN to extract features of specific retinal layer boundaries and train a corresponding classifier to delineate a pilot estimate of the eight layers. Next, a graph search method uses the probability maps created from the CNN to find the final boundaries. We validated our proposed method on 60 volumes (2915 B-scans) from 20 human eyes with non-exudative age-related macular degeneration (AMD), which attested to effectiveness of our proposed technique.
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Affiliation(s)
- Leyuan Fang
- Departments of Biomedical Engineering Duke University, Durham, NC 27708, USA
- College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
| | - David Cunefare
- Departments of Biomedical Engineering Duke University, Durham, NC 27708, USA
| | - Chong Wang
- College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
| | - Robyn H. Guymer
- Centre for Eye Research Australia University of Melbourne, Department of Surgery, Royal Victorian Eye and Ear Hospital, Victoria 3002, Australia
| | - Shutao Li
- College of Electrical and Information Engineering, Hunan University, Changsha 410082, China
| | - Sina Farsiu
- Departments of Biomedical Engineering Duke University, Durham, NC 27708, USA
- Department of Ophthalmology, Duke University Medical Center, Durham, NC 27710, USA
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161
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Khalid S, Akram MU, Hassan T, Nasim A, Jameel A. Fully Automated Robust System to Detect Retinal Edema, Central Serous Chorioretinopathy, and Age Related Macular Degeneration from Optical Coherence Tomography Images. BIOMED RESEARCH INTERNATIONAL 2017; 2017:7148245. [PMID: 28424788 PMCID: PMC5382397 DOI: 10.1155/2017/7148245] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/27/2016] [Revised: 02/23/2017] [Accepted: 03/08/2017] [Indexed: 11/18/2022]
Abstract
Maculopathy is the excessive damage to macula that leads to blindness. It mostly occurs due to retinal edema (RE), central serous chorioretinopathy (CSCR), or age related macular degeneration (ARMD). Optical coherence tomography (OCT) imaging is the latest eye testing technique that can detect these syndromes in early stages. Many researchers have used OCT images to detect retinal abnormalities. However, to the best of our knowledge, no research that presents a fully automated system to detect all of these macular syndromes is reported. This paper presents the world's first ever decision support system to automatically detect RE, CSCR, and ARMD retinal pathologies and healthy retina from OCT images. The automated disease diagnosis in our proposed system is based on multilayered support vector machines (SVM) classifier trained on 40 labeled OCT scans (10 healthy, 10 RE, 10 CSCR, and 10 ARMD). After training, SVM forms an accurate decision about the type of retinal pathology using 9 extracted features. We have tested our proposed system on 2819 OCT scans (1437 healthy, 640 RE, and 742 CSCR) of 502 patients from two different datasets and our proposed system correctly diagnosed 2817/2819 subjects with the accuracy, sensitivity, and specificity ratings of 99.92%, 100%, and 99.86%, respectively.
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Affiliation(s)
- Samina Khalid
- Department of Computer Science & Information Technology, Mirpur University of Science and Technology, Mirpur, Pakistan
- Department of Software Engineering, Bahria University, Islamabad, Pakistan
| | - M. Usman Akram
- Department of Computer Engineering, National University of Sciences and Technology, Islamabad, Pakistan
| | - Taimur Hassan
- Department of Computer Engineering, National University of Sciences and Technology, Islamabad, Pakistan
- Department of Electrical Engineering, Bahria University, Islamabad, Pakistan
| | - Ammara Nasim
- Department of Electrical Engineering, Bahria University, Islamabad, Pakistan
| | - Amina Jameel
- Department of Computer Engineering, Bahria University, Islamabad, Pakistan
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162
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Lee CS, Baughman DM, Lee AY. Deep learning is effective for the classification of OCT images of normal versus Age-related Macular Degeneration. Ophthalmol Retina 2017; 1:322-327. [PMID: 30693348 DOI: 10.1016/j.oret.2016.12.009] [Citation(s) in RCA: 295] [Impact Index Per Article: 42.1] [Reference Citation Analysis] [Abstract] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
Objective The advent of Electronic Medical Records (EMR) with large electronic imaging databases along with advances in deep neural networks with machine learning has provided a unique opportunity to achieve milestones in automated image analysis. Optical coherence tomography (OCT) is the most commonly obtained imaging modality in ophthalmology and represents a dense and rich dataset when combined with labels derived from the EMR. We sought to determine if deep learning could be utilized to distinguish normal OCT images from images from patients with Age-related Macular Degeneration (AMD). Design EMR and OCT database study. Subjects Normal and AMD patients who had a macular OCT. Methods Automated extraction of an OCT imaging database was performed and linked to clinical endpoints from the EMR. OCT macula scans were obtained by Heidelberg Spectralis, and each OCT scan was linked to EMR clinical endpoints extracted from EPIC. The central 11 images were selected from each OCT scan of two cohorts of patients: normal and AMD. Cross-validation was performed using a random subset of patients. Receiver operator curves (ROC) were constructed at an independent image level, macular OCT level, and patient level. Main outcome measure Area under the ROC. Results Of a recent extraction of 2.6 million OCT images linked to clinical datapoints from the EMR, 52,690 normal macular OCT images and 48,312 AMD macular OCT images were selected. A deep neural network was trained to categorize images as either normal or AMD. At the image level, we achieved an area under the ROC of 92.78% with an accuracy of 87.63%. At the macula level, we achieved an area under the ROC of 93.83% with an accuracy of 88.98%. At a patient level, we achieved an area under the ROC of 97.45% with an accuracy of 93.45%. Peak sensitivity and specificity with optimal cutoffs were 92.64% and 93.69% respectively. Conclusions Deep learning techniques achieve high accuracy and is effective as a new image classification technique. These findings have important implications in utilizing OCT in automated screening and the development of computer aided diagnosis tools in the future.
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Affiliation(s)
- Cecilia S Lee
- Department of Ophthalmology, University of Washington School of Medicine, Seattle WA
| | - Doug M Baughman
- Department of Ophthalmology, University of Washington School of Medicine, Seattle WA
| | - Aaron Y Lee
- Department of Ophthalmology, University of Washington School of Medicine, Seattle WA
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163
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Karri SPK, Chakraborty D, Chatterjee J. Transfer learning based classification of optical coherence tomography images with diabetic macular edema and dry age-related macular degeneration. BIOMEDICAL OPTICS EXPRESS 2017; 8:579-592. [PMID: 28270969 PMCID: PMC5330546 DOI: 10.1364/boe.8.000579] [Citation(s) in RCA: 120] [Impact Index Per Article: 17.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2016] [Revised: 12/12/2016] [Accepted: 12/13/2016] [Indexed: 05/06/2023]
Abstract
We present an algorithm for identifying retinal pathologies given retinal optical coherence tomography (OCT) images. Our approach fine-tunes a pre-trained convolutional neural network (CNN), GoogLeNet, to improve its prediction capability (compared to random initialization training) and identifies salient responses during prediction to understand learned filter characteristics. We considered a data set containing subjects with diabetic macular edema, or dry age-related macular degeneration, or no pathology. The fine-tuned CNN could effectively identify pathologies in comparison to classical learning. Our algorithm aims to demonstrate that models trained on non-medical images can be fine-tuned for classifying OCT images with limited training data.
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Affiliation(s)
- S. P. K. Karri
- School of Medical Science and Technology, IIT Kharagpur, Kharagpur, India
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164
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Sidibé D, Sankar S, Lemaître G, Rastgoo M, Massich J, Cheung CY, Tan GSW, Milea D, Lamoureux E, Wong TY, Mériaudeau F. An anomaly detection approach for the identification of DME patients using spectral domain optical coherence tomography images. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017; 139:109-117. [PMID: 28187882 DOI: 10.1016/j.cmpb.2016.11.001] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2016] [Revised: 09/02/2016] [Accepted: 11/01/2016] [Indexed: 05/13/2023]
Abstract
This paper proposes a method for automatic classification of spectral domain OCT data for the identification of patients with retinal diseases such as Diabetic Macular Edema (DME). We address this issue as an anomaly detection problem and propose a method that not only allows the classification of the OCT volume, but also allows the identification of the individual diseased B-scans inside the volume. Our approach is based on modeling the appearance of normal OCT images with a Gaussian Mixture Model (GMM) and detecting abnormal OCT images as outliers. The classification of an OCT volume is based on the number of detected outliers. Experimental results with two different datasets show that the proposed method achieves a sensitivity and a specificity of 80% and 93% on the first dataset, and 100% and 80% on the second one. Moreover, the experiments show that the proposed method achieves better classification performance than other recently published works.
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Affiliation(s)
- Désiré Sidibé
- LE2I, UMR6306, CNRS, Arts et Métiers, Université de Bourgogne-Franche Comté, F-21000 Dijon, France.
| | - Shrinivasan Sankar
- LE2I, UMR6306, CNRS, Arts et Métiers, Université de Bourgogne-Franche Comté, F-21000 Dijon, France
| | - Guillaume Lemaître
- LE2I, UMR6306, CNRS, Arts et Métiers, Université de Bourgogne-Franche Comté, F-21000 Dijon, France
| | - Mojdeh Rastgoo
- LE2I, UMR6306, CNRS, Arts et Métiers, Université de Bourgogne-Franche Comté, F-21000 Dijon, France
| | - Joan Massich
- LE2I, UMR6306, CNRS, Arts et Métiers, Université de Bourgogne-Franche Comté, F-21000 Dijon, France
| | - Carol Y Cheung
- Singaore Eye Research Institute, Singapore National Eye Center, Singapore; Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong
| | - Gavin S W Tan
- Singaore Eye Research Institute, Singapore National Eye Center, Singapore
| | - Dan Milea
- Singaore Eye Research Institute, Singapore National Eye Center, Singapore
| | - Ecosse Lamoureux
- Singaore Eye Research Institute, Singapore National Eye Center, Singapore
| | - Tien Y Wong
- Singaore Eye Research Institute, Singapore National Eye Center, Singapore
| | - Fabrice Mériaudeau
- LE2I, UMR6306, CNRS, Arts et Métiers, Université de Bourgogne-Franche Comté, F-21000 Dijon, France; Center for Intelligent Signal and Imaging Research (CISIR), EEE Department, Universiti Teknologi Petronas, 32610 Seri Iskandar, Perak, Malaysia
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165
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Sun Y, Li S, Sun Z. Fully automated macular pathology detection in retina optical coherence tomography images using sparse coding and dictionary learning. JOURNAL OF BIOMEDICAL OPTICS 2017; 22:16012. [PMID: 28114453 DOI: 10.1117/1.jbo.22.1.016012] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/03/2016] [Accepted: 12/27/2016] [Indexed: 05/04/2023]
Abstract
We propose a framework for automated detection of dry age-related macular degeneration (AMD) and diabetic macular edema (DME) from retina optical coherence tomography (OCT) images, based on sparse coding and dictionary learning. The study aims to improve the classification performance of state-of-the-art methods. First, our method presents a general approach to automatically align and crop retina regions; then it obtains global representations of images by using sparse coding and a spatial pyramid; finally, a multiclass linear support vector machine classifier is employed for classification. We apply two datasets for validating our algorithm: Duke spectral domain OCT (SD-OCT) dataset, consisting of volumetric scans acquired from 45 subjects—15 normal subjects, 15 AMD patients, and 15 DME patients; and clinical SD-OCT dataset, consisting of 678 OCT retina scans acquired from clinics in Beijing—168, 297, and 213 OCT images for AMD, DME, and normal retinas, respectively. For the former dataset, our classifier correctly identifies 100%, 100%, and 93.33% of the volumes with DME, AMD, and normal subjects, respectively, and thus performs much better than the conventional method; for the latter dataset, our classifier leads to a correct classification rate of 99.67%, 99.67%, and 100.00% for DME, AMD, and normal images, respectively.
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Affiliation(s)
- Yankui Sun
- Tsinghua University, Department of Computer Science and Technology, 30 Shuangqing Road, Haidian District, Beijing 100084, China
| | - Shan Li
- Tsinghua University, Department of Computer Science and Technology, 30 Shuangqing Road, Haidian District, Beijing 100084, ChinabBeihang University, School of Software, 37 Xueyuan Road, Haidian District, Beijing 100191, China
| | - Zhongyang Sun
- Tsinghua University, Department of Computer Science and Technology, 30 Shuangqing Road, Haidian District, Beijing 100084, ChinacSun Yat-Sen University, School of Data and Computer Science, 132 East Waihuan Road, Guangzhou Higher Education Mega Center (University Town), Guangzhou 510006, China
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166
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Wang Y, Zhang Y, Yao Z, Zhao R, Zhou F. Machine learning based detection of age-related macular degeneration (AMD) and diabetic macular edema (DME) from optical coherence tomography (OCT) images. BIOMEDICAL OPTICS EXPRESS 2016; 7:4928-4940. [PMID: 28018716 PMCID: PMC5175542 DOI: 10.1364/boe.7.004928] [Citation(s) in RCA: 50] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/24/2016] [Revised: 10/05/2016] [Accepted: 10/05/2016] [Indexed: 05/05/2023]
Abstract
Non-lethal macular diseases greatly impact patients' life quality, and will cause vision loss at the late stages. Visual inspection of the optical coherence tomography (OCT) images by the experienced clinicians is the main diagnosis technique. We proposed a computer-aided diagnosis (CAD) model to discriminate age-related macular degeneration (AMD), diabetic macular edema (DME) and healthy macula. The linear configuration pattern (LCP) based features of the OCT images were screened by the Correlation-based Feature Subset (CFS) selection algorithm. And the best model based on the sequential minimal optimization (SMO) algorithm achieved 99.3% in the overall accuracy for the three classes of samples.
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Affiliation(s)
- Yu Wang
- Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang, Liaoning 110169, China; Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, China
| | - Yaonan Zhang
- Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang, Liaoning 110169, China; College of Electronics and Information Engineering, Xi'an Siyuan University, Xi'an 710038, China;
| | - Zhaomin Yao
- Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang, Liaoning 110169, China; Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, China
| | - Ruixue Zhao
- College of Computer Science and Technology, Jilin University, Changchun, Jilin 130012, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, China
| | - Fengfeng Zhou
- College of Computer Science and Technology, Jilin University, Changchun, Jilin 130012, China; Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun, Jilin 130012, China; ; ; http://www.healthinformaticslab.org/ffzhou/
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167
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Syed AM, Hassan T, Akram MU, Naz S, Khalid S. Automated diagnosis of macular edema and central serous retinopathy through robust reconstruction of 3D retinal surfaces. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2016; 137:1-10. [PMID: 28110716 DOI: 10.1016/j.cmpb.2016.09.004] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2016] [Accepted: 09/07/2016] [Indexed: 06/06/2023]
Abstract
BACKGROUND AND OBJECTIVES Macular diseases tend to damage macula within human retina due to which the central vision of a person is affected. Macular edema (ME) and central serous retinopathy (CSR) are two of the most common macular diseases. Many researchers worked on automated detection of ME from optical coherence tomography (OCT) and fundus images, whereas few researchers have worked on diagnosing central serous retinopathy. But this paper proposes a fully automated method for the classification of ME and CSR through robust reconstruction of 3D OCT retinal surfaces. METHODS The proposed system uses structure tensors to extract retinal layers from OCT images. The 3D retinal surface is then reconstructed by extracting the brightness scan (B-scan) thickness profile from each coherent tensor. The proposed system extracts 8 distinct features (3 based on retinal thickness profile of right side, 3 based on thickness profile of left side and 2 based on top surface and cyst spaces within retinal layers) from 30 labeled volumes (10 healthy, 10 CSR and 10 ME) which are used to train the supervised support vector machines (SVM) classifier. RESULTS In this research we have considered 90 OCT volumes (30 Healthy, 30 CSR and 30 ME) of 73 patients to test the proposed system where our proposed system correctly classified 89 out of 90 cases and has promising receiver operator characteristics (ROC) ratings with accuracy, sensitivity and specificity of 98.88%, 100%, and 96.66% respectively. CONCLUSION The proposed system is quite fast and robust in detecting all the three types of retinal pathologies from volumetric OCT scans. The proposed system is fully automated and provides an early and on fly diagnosis of ME and CSR syndromes. 3D macular thickness surfaces can further be used as decision support parameter in clinical studies to check the volume of cyst.
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Affiliation(s)
- Adeel M Syed
- Department of Software Engineering, Bahria University, Islamabad, Pakistan
| | - Taimur Hassan
- Department of Electrical Engineering, Bahria University, Islamabad, Pakistan.
| | - M Usman Akram
- Department of Computer Engineering, National University of Sciences and Technology, Islamabad, Pakistan
| | - Samra Naz
- Department of Computer Engineering, National University of Sciences and Technology, Islamabad, Pakistan
| | - Shehzad Khalid
- Department of Computer Engineering, Bahria University, Islamabad, Pakistan
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168
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Keane PA, Grossi CM, Foster PJ, Yang Q, Reisman CA, Chan K, Peto T, Thomas D, Patel PJ. Optical Coherence Tomography in the UK Biobank Study - Rapid Automated Analysis of Retinal Thickness for Large Population-Based Studies. PLoS One 2016; 11:e0164095. [PMID: 27716837 PMCID: PMC5055325 DOI: 10.1371/journal.pone.0164095] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2015] [Accepted: 09/20/2016] [Indexed: 12/16/2022] Open
Abstract
PURPOSE To describe an approach to the use of optical coherence tomography (OCT) imaging in large, population-based studies, including methods for OCT image acquisition, storage, and the remote, rapid, automated analysis of retinal thickness. METHODS In UK Biobank, OCT images were acquired between 2009 and 2010 using a commercially available "spectral domain" OCT device (3D OCT-1000, Topcon). Images were obtained using a raster scan protocol, 6 mm x 6 mm in area, and consisting of 128 B-scans. OCT image sets were stored on UK Biobank servers in a central repository, adjacent to high performance computers. Rapid, automated analysis of retinal thickness was performed using custom image segmentation software developed by the Topcon Advanced Biomedical Imaging Laboratory (TABIL). This software employs dual-scale gradient information to allow for automated segmentation of nine intraretinal boundaries in a rapid fashion. RESULTS 67,321 participants (134,642 eyes) in UK Biobank underwent OCT imaging of both eyes as part of the ocular module. 134,611 images were successfully processed with 31 images failing segmentation analysis due to corrupted OCT files or withdrawal of subject consent for UKBB study participation. Average time taken to call up an image from the database and complete segmentation analysis was approximately 120 seconds per data set per login, and analysis of the entire dataset was completed in approximately 28 days. CONCLUSIONS We report an approach to the rapid, automated measurement of retinal thickness from nearly 140,000 OCT image sets from the UK Biobank. In the near future, these measurements will be publically available for utilization by researchers around the world, and thus for correlation with the wealth of other data collected in UK Biobank. The automated analysis approaches we describe may be of utility for future large population-based epidemiological studies, clinical trials, and screening programs that employ OCT imaging.
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Affiliation(s)
- Pearse A. Keane
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, United Kingdom
| | - Carlota M. Grossi
- Division of Genetics & Epidemiology, UCL Institute of Ophthalmology, London, United Kingdom
| | - Paul J. Foster
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, United Kingdom
- Division of Genetics & Epidemiology, UCL Institute of Ophthalmology, London, United Kingdom
| | - Qi Yang
- Topcon Advanced Biomedical Imaging Laboratory, Oakland, New Jersey, United States of America
| | - Charles A. Reisman
- Topcon Advanced Biomedical Imaging Laboratory, Oakland, New Jersey, United States of America
| | - Kinpui Chan
- Topcon Advanced Biomedical Imaging Laboratory, Oakland, New Jersey, United States of America
| | - Tunde Peto
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, United Kingdom
| | - Dhanes Thomas
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, United Kingdom
| | - Praveen J. Patel
- NIHR Biomedical Research Centre, Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, United Kingdom
- * E-mail:
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169
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Alsaih K, Lemaitre G, Vall JM, Rastgoo M, Sidibe D, Wong TY, Lamoureux E, Milea D, Cheung CY, Meriaudeau F. Classification of SD-OCT volumes with multi pyramids, LBP and HOG descriptors: application to DME detections. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2016; 2016:1344-1347. [PMID: 28268574 DOI: 10.1109/embc.2016.7590956] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
This paper deals with the automated detection of Diabetic Macular Edema (DME) on Optical Coherence Tomography (OCT) volumes. Our method considers a generic classification pipeline with preprocessing for noise removal and flattening of each B-Scan. Features such as Histogram of Oriented Gradients (HOG) and Local Binary Patterns (LBP) are extracted and combined to create a set of different feature vectors which are fed to a linear-Support Vector Machines (SVM) Classifier. Experimental results show a promising sensitivity/specificity of 0.75/0.87 on a challenging dataset.
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170
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Classification of SD-OCT Volumes Using Local Binary Patterns: Experimental Validation for DME Detection. J Ophthalmol 2016; 2016:3298606. [PMID: 27555965 PMCID: PMC4983398 DOI: 10.1155/2016/3298606] [Citation(s) in RCA: 56] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2015] [Revised: 02/15/2016] [Accepted: 05/24/2016] [Indexed: 11/18/2022] Open
Abstract
This paper addresses the problem of automatic classification of Spectral Domain OCT (SD-OCT) data for automatic identification of patients with DME versus normal subjects. Optical Coherence Tomography (OCT) has been a valuable diagnostic tool for DME, which is among the most common causes of irreversible vision loss in individuals with diabetes. Here, a classification framework with five distinctive steps is proposed and we present an extensive study of each step. Our method considers combination of various preprocessing steps in conjunction with Local Binary Patterns (LBP) features and different mapping strategies. Using linear and nonlinear classifiers, we tested the developed framework on a balanced cohort of 32 patients. Experimental results show that the proposed method outperforms the previous studies by achieving a Sensitivity (SE) and a Specificity (SP) of 81.2% and 93.7%, respectively. Our study concludes that the 3D features and high-level representation of 2D features using patches achieve the best results. However, the effects of preprocessing are inconsistent with different classifiers and feature configurations.
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171
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Keller B, Cunefare D, Grewal DS, Mahmoud TH, Izatt JA, Farsiu S. Length-adaptive graph search for automatic segmentation of pathological features in optical coherence tomography images. JOURNAL OF BIOMEDICAL OPTICS 2016; 21:76015. [PMID: 27533243 PMCID: PMC4963530 DOI: 10.1117/1.jbo.21.7.076015] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/03/2016] [Accepted: 07/11/2016] [Indexed: 05/20/2023]
Abstract
We introduce a metric in graph search and demonstrate its application for segmenting retinal optical coherence tomography (OCT) images of macular pathology. Our proposed “adjusted mean arc length” (AMAL) metric is an adaptation of the lowest mean arc length search technique for automated OCT segmentation. We compare this method to Dijkstra’s shortest path algorithm, which we utilized previously in our popular graph theory and dynamic programming segmentation technique. As an illustrative example, we show that AMAL-based length-adaptive segmentation outperforms the shortest path in delineating the retina/vitreous boundary of patients with full-thickness macular holes when compared with expert manual grading.
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Affiliation(s)
- Brenton Keller
- Duke University, Department of Biomedical Engineering, 101 Science Drive, Campus Box 90281, Durham, North Carolina 27708, United States
- Address all correspondence to: Brenton Keller, E-mail:
| | - David Cunefare
- Duke University, Department of Biomedical Engineering, 101 Science Drive, Campus Box 90281, Durham, North Carolina 27708, United States
| | - Dilraj S. Grewal
- Duke University, Department of Ophthalmology, Duke University Medical Center, Durham, North Carolina 27710, United States
| | - Tamer H. Mahmoud
- Duke University, Department of Ophthalmology, Duke University Medical Center, Durham, North Carolina 27710, United States
| | - Joseph A. Izatt
- Duke University, Department of Biomedical Engineering, 101 Science Drive, Campus Box 90281, Durham, North Carolina 27708, United States
- Duke University, Department of Ophthalmology, Duke University Medical Center, Durham, North Carolina 27710, United States
| | - Sina Farsiu
- Duke University, Department of Biomedical Engineering, 101 Science Drive, Campus Box 90281, Durham, North Carolina 27708, United States
- Duke University, Department of Ophthalmology, Duke University Medical Center, Durham, North Carolina 27710, United States
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172
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Hassan B, Raja G, Hassan T, Usman Akram M. Structure tensor based automated detection of macular edema and central serous retinopathy using optical coherence tomography images. JOURNAL OF THE OPTICAL SOCIETY OF AMERICA. A, OPTICS, IMAGE SCIENCE, AND VISION 2016; 33:455-63. [PMID: 27140751 DOI: 10.1364/josaa.33.000455] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2023]
Abstract
Macular edema (ME) and central serous retinopathy (CSR) are two macular diseases that affect the central vision of a person if they are left untreated. Optical coherence tomography (OCT) imaging is the latest eye examination technique that shows a cross-sectional region of the retinal layers and that can be used to detect many retinal disorders in an early stage. Many researchers have done clinical studies on ME and CSR and reported significant findings in macular OCT scans. However, this paper proposes an automated method for the classification of ME and CSR from OCT images using a support vector machine (SVM) classifier. Five distinct features (three based on the thickness profiles of the sub-retinal layers and two based on cyst fluids within the sub-retinal layers) are extracted from 30 labeled images (10 ME, 10 CSR, and 10 healthy), and SVM is trained on these. We applied our proposed algorithm on 90 time-domain OCT (TD-OCT) images (30 ME, 30 CSR, 30 healthy) of 73 patients. Our algorithm correctly classified 88 out of 90 subjects with accuracy, sensitivity, and specificity of 97.77%, 100%, and 93.33%, respectively.
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173
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van Grinsven MJJP, Theelen T, Witkamp L, van der Heijden J, van de Ven JPH, Hoyng CB, van Ginneken B, Sánchez CI. Automatic differentiation of color fundus images containing drusen or exudates using a contextual spatial pyramid approach. BIOMEDICAL OPTICS EXPRESS 2016; 7:709-25. [PMID: 27231583 PMCID: PMC4866450 DOI: 10.1364/boe.7.000709] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/26/2015] [Revised: 01/22/2016] [Accepted: 01/23/2016] [Indexed: 05/11/2023]
Abstract
We developed an automatic system to identify and differentiate color fundus images containing no lesions, drusen or exudates. Drusen and exudates are lesions with a bright appearance, associated with age-related macular degeneration and diabetic retinopathy, respectively. The system consists of three lesion detectors operating at pixel-level, combining their outputs using spatial pooling and classification with a random forest classifier. System performance was compared with ratings of two independent human observers using human-expert annotations as reference. Kappa agreements of 0.89, 0.97 and 0.92 and accuracies of 0.93, 0.98 and 0.95 were obtained for the system and observers, respectively.
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Affiliation(s)
- Mark J. J. P. van Grinsven
- Diagnostic Image Analysis Group, Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The
Netherlands
| | - Thomas Theelen
- Department of Ophthalmology, Radboud University Medical Center, Nijmegen, The
Netherlands
| | | | | | | | - Carel B. Hoyng
- Department of Ophthalmology, Radboud University Medical Center, Nijmegen, The
Netherlands
| | - Bram van Ginneken
- Diagnostic Image Analysis Group, Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The
Netherlands
| | - Clara I. Sánchez
- Diagnostic Image Analysis Group, Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The
Netherlands
- Department of Ophthalmology, Radboud University Medical Center, Nijmegen, The
Netherlands
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174
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Hassan T, Akram MU, Hassan B, Syed AM, Bazaz SA. Automated segmentation of subretinal layers for the detection of macular edema. APPLIED OPTICS 2016; 55:454-61. [PMID: 26835917 DOI: 10.1364/ao.55.000454] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
Macular edema (ME) is considered as one of the major indications of proliferative diabetic retinopathy and it is commonly caused due to diabetes. ME causes retinal swelling due to the accumulation of protein deposits within subretinal layers. Optical coherence tomography (OCT) imaging provides an early detection of ME by showing the cross-sectional view of macular pathology. Many researchers have worked on automated identification of macular edema from fundus images, but this paper proposes a fully automated method for extracting and analyzing subretinal layers from OCT images using coherent tensors. These subretinal layers are then used to predict ME from candidate images using a support vector machine (SVM) classifier. A total of 71 OCT images of 64 patients are collected locally in which 15 persons have ME and 49 persons are healthy. Our proposed system has an overall accuracy of 97.78% in correctly classifying ME patients and healthy persons. We have also tested our proposed implementation on spectral domain OCT (SD-OCT) images of the Duke dataset consisting of 109 images from 10 patients and it correctly classified all healthy and ME images in the dataset.
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175
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Yang S, Zuo C, Xiao H, Mi L, Luo G, Xu X, Liu X. Photoreceptor dysfunction in early and intermediate age-related macular degeneration assessed with mfERG and spectral domain OCT. Doc Ophthalmol 2016; 132:17-26. [PMID: 26754967 DOI: 10.1007/s10633-016-9523-4] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2015] [Accepted: 01/04/2016] [Indexed: 10/22/2022]
Abstract
PURPOSE To evaluate the changes of the photoreceptor layer (PRL) thickness with spectral domain optical coherence tomography (SD-OCT) and the retinal function by mfERG, as well as the correlation of morphology and function parameters in subjects with early and intermediate age-related macular degeneration (AMD). METHODS Subjects with clinical diagnosis of early or intermediate AMD and age-matched healthy subjects were recruited prospectively in this study. Color fundus photography, SD-OCT, and mfERG were conducted. Retinal photoreceptor thickness was measured, and first-order kernel responses were recorded. The differences between AMD group and control group were compared, and the correlations between macular photoreceptor thickness and the mfERG were analyzed. RESULTS PRL thickness (μm) in four areas including foveola and 0.5, 1.5, and 3 mm away from foveola was 192.48 ± 17.37, 163.73 ± 12.95, 130.93 ± 9.20, and 108.78 ± 7.81, respectively, in normal eyes, whereas in AMD group, they were 158.61 ± 45.25, 138.91 ± 20.92, 118.91 ± 12.85, and 95.00 ± 9.64, respectively (P < 0.001). The mean amplitude response densities of AMD patients decreased significantly compared to the control group in ring 1-6 (P < 0.001). The mean mfERG N1 and P1 latency of AMD patients prolonged compared to the control group, except the ring 1 (P = 0.588 and P = 0.084). The macular PRL thickness was significantly associated with the mfERGN1 and P1 amplitude density in ring 1-4 (r = 0.338-0.533, P < 0.01). CONCLUSIONS PRL thickness decreases are in accordance with the deterioration of retinal electrophysiological activity. The retinal PRL thickness is important parameter to assess of early and intermediate AMD severity.
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Affiliation(s)
- Shasha Yang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, 54 South Xianlie Road, Guangzhou, 510060, China
| | - Chengguo Zuo
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, 54 South Xianlie Road, Guangzhou, 510060, China
| | - Hui Xiao
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, 54 South Xianlie Road, Guangzhou, 510060, China
| | - Lan Mi
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, 54 South Xianlie Road, Guangzhou, 510060, China
| | - Guangwei Luo
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, 54 South Xianlie Road, Guangzhou, 510060, China
| | - Xiaoyu Xu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, 54 South Xianlie Road, Guangzhou, 510060, China
| | - Xing Liu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, 54 South Xianlie Road, Guangzhou, 510060, China.
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176
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Dittberner A, Rodner E, Ortmann W, Stadler J, Schmidt C, Petersen I, Stallmach A, Denzler J, Guntinas-Lichius O. Automated analysis of confocal laser endomicroscopy images to detect head and neck cancer. Head Neck 2015; 38 Suppl 1:E1419-26. [PMID: 26560348 DOI: 10.1002/hed.24253] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/25/2015] [Indexed: 12/24/2022] Open
Abstract
BACKGROUND The purpose of this study was to develop an automated image analysis algorithm to discriminate between head and neck cancer and nonneoplastic epithelium in confocal laser endomicroscopy (CLE) images. METHODS CLE was applied to image head and neck cancer epithelium in vivo. Histopathologic diagnosis from biopsies was used to classify the CLE images offline as cancer or noncancer tissue. The classified images were used to train automated software based on distance map histograms. The performance of the final algorithm was confirmed by "leave 2 patients out" cross-validation and area under the curve (AUC)/receiver operating characteristic (ROC) analysis. RESULTS Ninety-two CLE videos and 92 biopsies were analyzed from 12 patients. One hundred two frames of classified neoplastic tissue and 52 frames of nonneoplastic tissue were used for cross-validation of the developed algorithm. AUC varied from 0.52 to 0.92. CONCLUSION The proposed software allows an objective classification of CLE images of head and neck cancer and adjacent nonneoplastic epithelium. © 2015 Wiley Periodicals, Inc. Head Neck 38: E1419-E1426, 2016.
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Affiliation(s)
- Andreas Dittberner
- Department of Otorhinolaryngology, Jena University Hospital, Jena, Germany.,Department of Otorhinolaryngology, Head and Neck Surgery, University of Erlangen-Nuremberg, Erlangen, Germany
| | - Erik Rodner
- Department of Computer Science, Friedrich Schiller University, Jena, Germany
| | - Wolfgang Ortmann
- Department of Computer Science, Friedrich Schiller University, Jena, Germany
| | - Joachim Stadler
- Department of Otorhinolaryngology, Jena University Hospital, Jena, Germany.,Department of Otorhinolaryngology, Heinrich-Braun-Klinikum, Zwickau, Germany
| | - Carsten Schmidt
- Division of Gastroenterology, Hepatology and Infectious Diseases, Department of Internal Medicine IV, Jena University Hospital, Jena, Germany
| | - Iver Petersen
- Institute of Pathology, Jena University Hospital, Jena, Germany
| | - Andreas Stallmach
- Division of Gastroenterology, Hepatology and Infectious Diseases, Department of Internal Medicine IV, Jena University Hospital, Jena, Germany
| | - Joachim Denzler
- Department of Computer Science, Friedrich Schiller University, Jena, Germany
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