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He Y, Carass A, Solomon SD, Saidha S, Calabresi PA, Prince JL. Retinal layer parcellation of optical coherence tomography images: Data resource for multiple sclerosis and healthy controls. Data Brief 2019; 22:601-604. [PMID: 30671506 PMCID: PMC6327073 DOI: 10.1016/j.dib.2018.12.073] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2018] [Revised: 12/14/2018] [Accepted: 12/20/2018] [Indexed: 11/20/2022] Open
Abstract
This paper presents optical coherence tomography (OCT) images of the human retina and manual delineations of eight retinal layers. The data includes 35 human retina scans acquired on a Spectralis OCT system (Heidelberg Engineering, Heidelberg, Germany), 14 of which are healthy controls (HC) and 21 have a diagnosis of multiple sclerosis (MS). The provided data includes manually delineation of eight retina layers, which were independently reviewed and edited. The data presented in this article was used to validate automatic segmentation algorithms (Lang et al., 2013).
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Affiliation(s)
- Yufan He
- Dept. of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA
| | - Aaron Carass
- Dept. of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA
| | - Sharon D. Solomon
- Wilmer Eye Institute, The Johns Hopkins School of Medicine, Baltimore, MD 21287, USA
| | - Shiv Saidha
- Dept. of Neurology, The Johns Hopkins School of Medicine, Baltimore, MD 21287, USA
| | - Peter A. Calabresi
- Dept. of Neurology, The Johns Hopkins School of Medicine, Baltimore, MD 21287, USA
| | - Jerry L. Prince
- Dept. of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA
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Lang A, Carass A, Jedynak BM, Solomon SD, Calabresi PA, Prince JL. Intensity inhomogeneity correction of SD-OCT data using macular flatspace. Med Image Anal 2018; 43:85-97. [PMID: 29040910 PMCID: PMC6311386 DOI: 10.1016/j.media.2017.09.008] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2017] [Revised: 09/25/2017] [Accepted: 09/29/2017] [Indexed: 01/12/2023]
Abstract
Images of the retina acquired using optical coherence tomography (OCT) often suffer from intensity inhomogeneity problems that degrade both the quality of the images and the performance of automated algorithms utilized to measure structural changes. This intensity variation has many causes, including off-axis acquisition, signal attenuation, multi-frame averaging, and vignetting, making it difficult to correct the data in a fundamental way. This paper presents a method for inhomogeneity correction by acting to reduce the variability of intensities within each layer. In particular, the N3 algorithm, which is popular in neuroimage analysis, is adapted to work for OCT data. N3 works by sharpening the intensity histogram, which reduces the variation of intensities within different classes. To apply it here, the data are first converted to a standardized space called macular flat space (MFS). MFS allows the intensities within each layer to be more easily normalized by removing the natural curvature of the retina. N3 is then run on the MFS data using a modified smoothing model, which improves the efficiency of the original algorithm. We show that our method more accurately corrects gain fields on synthetic OCT data when compared to running N3 on non-flattened data. It also reduces the overall variability of the intensities within each layer, without sacrificing contrast between layers, and improves the performance of registration between OCT images.
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Affiliation(s)
- Andrew Lang
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA.
| | - Aaron Carass
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA; Department of Computer Science, The Johns Hopkins University, Baltimore, MD 21218, USA.
| | - Bruno M Jedynak
- Department of Mathematics and Statistics, Portland State University, Portland, OR 97201, USA.
| | - Sharon D Solomon
- Department of Ophthalmology, The Johns Hopkins School of Medicine, Baltimore, MD 21287, USA.
| | - Peter A Calabresi
- Department of Neurology, The Johns Hopkins School of Medicine, Baltimore, MD 21287, USA.
| | - Jerry L Prince
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA.
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Antony BJ, Chen M, Carass A, Jedynak BM, Al-Louzi O, Solomon SD, Saidha S, Calabresi PA, Prince JL. Voxel Based Morphometry in Optical Coherence Tomography: Validation & Core Findings. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2016; 9788. [PMID: 27199503 DOI: 10.1117/12.2216096] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Optical coherence tomography (OCT) of the human retina is now becoming established as an important modality for the detection and tracking of various ocular diseases. Voxel based morphometry (VBM) is a long standing neuroimaging analysis technique that allows for the exploration of the regional differences in the brain. There has been limited work done in developing registration based methods for OCT, which has hampered the advancement of VBM analyses in OCT based population studies. Following on from our recent development of an OCT registration method, we explore the potential benefits of VBM analysis in cohorts of healthy controls (HCs) and multiple sclerosis (MS) patients. Specifically, we validate the stability of VBM analysis in two pools of HCs showing no significant difference between the two populations. Additionally, we also present a retrospective study of age and sex matched HCs and relapsing remitting MS patients, demonstrating results consistent with the reported literature while providing insight into the retinal changes associated with this MS subtype.
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Affiliation(s)
- Bhavna J Antony
- Department of Electrical and Computer Engineering, Johns Hopkins University
| | - Min Chen
- Penn Image Computing and Science Laboratory, The University of Pennsylvania
| | - Aaron Carass
- Department of Electrical and Computer Engineering, Johns Hopkins University
| | | | - Omar Al-Louzi
- Department of Neurology, Johns Hopkins School of Medicine
| | | | - Shiv Saidha
- Department of Neurology, Johns Hopkins School of Medicine
| | | | - Jerry L Prince
- Department of Electrical and Computer Engineering, Johns Hopkins University
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Lee S, Lebed E, Sarunic MV, Beg MF. Exact surface registration of retinal surfaces from 3-D optical coherence tomography images. IEEE Trans Biomed Eng 2014; 62:609-17. [PMID: 25312906 DOI: 10.1109/tbme.2014.2361778] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Nonrigid registration of optical coherence tomography (OCT) images is an important problem in studying eye diseases, evaluating the effect of pharmaceuticals in treating vision loss, and performing group-wise cross-sectional analysis. High dimensional nonrigid registration algorithms required for cross-sectional and longitudinal analysis are still being developed for accurate registration of OCT image volumes, with the speckle noise in images presenting a challenge for registration. Development of algorithms for segmentation of OCT images to generate surface models of retinal layers has advanced considerably and several algorithms are now available that can segment retinal OCT images into constituent retinal surfaces. Important morphometric measurements can be extracted if accurate surface registration algorithm for registering retinal surfaces onto corresponding template surfaces were available. In this paper, we present a novel method to perform multiple and simultaneous retinal surface registration, targeted to registering surfaces extracted from ocular volumetric OCT images. This enables a point-to-point correspondence (homology) between template and subject surfaces, allowing for a direct, vertex-wise comparison of morphometric measurements across subject groups. We demonstrate that this approach can be used to localize and analyze regional changes in choroidal and nerve fiber layer thickness among healthy and glaucomatous subjects, allowing for cross-sectional population wise analysis. We also demonstrate the method's ability to track longitudinal changes in optic nerve head morphometry, allowing for within-individual tracking of morphometric changes. This method can also, in the future, be used as a precursor to 3-D OCT image registration to better initialize nonrigid image registration algorithms closer to the desired solution.
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Chen M, Lang A, Ying HS, Calabresi PA, Prince JL, Carass A. Analysis of macular OCT images using deformable registration. BIOMEDICAL OPTICS EXPRESS 2014; 5:2196-214. [PMID: 25071959 PMCID: PMC4102359 DOI: 10.1364/boe.5.002196] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/21/2014] [Revised: 05/30/2014] [Accepted: 06/02/2014] [Indexed: 05/05/2023]
Abstract
Optical coherence tomography (OCT) of the macula has become increasingly important in the investigation of retinal pathology. However, deformable image registration, which is used for aligning subjects for pairwise comparisons, population averaging, and atlas label transfer, has not been well-developed and demonstrated on OCT images. In this paper, we present a deformable image registration approach designed specifically for macular OCT images. The approach begins with an initial translation to align the fovea of each subject, followed by a linear rescaling to align the top and bottom retinal boundaries. Finally, the layers within the retina are aligned by a deformable registration using one-dimensional radial basis functions. The algorithm was validated using manual delineations of retinal layers in OCT images from a cohort consisting of healthy controls and patients diagnosed with multiple sclerosis (MS). We show that the algorithm overcomes the shortcomings of existing generic registration methods, which cannot be readily applied to OCT images. A successful deformable image registration algorithm for macular OCT opens up a variety of population based analysis techniques that are regularly used in other imaging modalities, such as spatial normalization, statistical atlas creation, and voxel based morphometry. Examples of these applications are provided to demonstrate the potential benefits such techniques can have on our understanding of retinal disease. In particular, included is a pilot study of localized volumetric changes between healthy controls and MS patients using the proposed registration algorithm.
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Affiliation(s)
- Min Chen
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD, 21218,
USA
- Translational Neuroradiology Unit, National Institute of Neurological Disorders and Stroke, Bethesda, MD 20892,
USA
| | - Andrew Lang
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD, 21218,
USA
| | - Howard S. Ying
- Wilmer Eye Institute, The Johns Hopkins School of Medicine, Baltimore, MD 21287,
USA
| | - Peter A. Calabresi
- Department of Neurology, The Johns Hopkins School of Medicine, Baltimore, MD 21287,
USA
| | - Jerry L. Prince
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD, 21218,
USA
| | - Aaron Carass
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD, 21218,
USA
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Carass A, Lang A, Hauser M, Calabresi PA, Ying HS, Prince JL. Multiple-object geometric deformable model for segmentation of macular OCT. BIOMEDICAL OPTICS EXPRESS 2014; 5:1062-74. [PMID: 24761289 PMCID: PMC3986003 DOI: 10.1364/boe.5.001062] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/01/2014] [Revised: 02/09/2014] [Accepted: 02/21/2014] [Indexed: 05/13/2023]
Abstract
Optical coherence tomography (OCT) is the de facto standard imaging modality for ophthalmological assessment of retinal eye disease, and is of increasing importance in the study of neurological disorders. Quantification of the thicknesses of various retinal layers within the macular cube provides unique diagnostic insights for many diseases, but the capability for automatic segmentation and quantification remains quite limited. While manual segmentation has been used for many scientific studies, it is extremely time consuming and is subject to intra- and inter-rater variation. This paper presents a new computational domain, referred to as flat space, and a segmentation method for specific retinal layers in the macular cube using a recently developed deformable model approach for multiple objects. The framework maintains object relationships and topology while preventing overlaps and gaps. The algorithm segments eight retinal layers over the whole macular cube, where each boundary is defined with subvoxel precision. Evaluation of the method on single-eye OCT scans from 37 subjects, each with manual ground truth, shows improvement over a state-of-the-art method.
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Affiliation(s)
- Aaron Carass
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD, 21218,
USA
| | - Andrew Lang
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD, 21218,
USA
| | - Matthew Hauser
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD, 21218,
USA
| | - Peter A. Calabresi
- Department of Neurology, The Johns Hopkins School of Medicine, Baltimore, MD 21287,
USA
| | - Howard S. Ying
- Wilmer Eye Institute, The Johns Hopkins School of Medicine Baltimore, MD 21287,
USA
| | - Jerry L. Prince
- Department of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD, 21218,
USA
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