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He Y, Carass A, Liu Y, Calabresi PA, Saidha S, Prince JL. Longitudinal deep network for consistent OCT layer segmentation. BIOMEDICAL OPTICS EXPRESS 2023; 14:1874-1893. [PMID: 37206119 PMCID: PMC10191669 DOI: 10.1364/boe.487518] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 03/11/2023] [Accepted: 03/17/2023] [Indexed: 05/21/2023]
Abstract
Retinal layer thickness is an important bio-marker for people with multiple sclerosis (PwMS). In clinical practice, retinal layer thickness changes in optical coherence tomography (OCT) are widely used for monitoring multiple sclerosis (MS) progression. Recent developments in automated retinal layer segmentation algorithms allow cohort-level retina thinning to be observed in a large study of PwMS. However, variability in these results make it difficult to identify patient-level trends; this prevents patient specific disease monitoring and treatment planning using OCT. Deep learning based retinal layer segmentation algorithms have achieved state-of-the-art accuracy, but the segmentation is performed on each individual scan without utilizing longitudinal information, which can be important in reducing segmentation error and reveal subtle changes in retinal layers. In this paper, we propose a longitudinal OCT segmentation network which achieves more accurate and consistent layer thickness measurements for PwMS.
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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
| | - Yihao Liu
- Dept. of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA
| | - Peter A. Calabresi
- Dept. of Neurology, The Johns Hopkins University School of Medicine, MD 21287, USA
| | - Shiv Saidha
- Dept. of Neurology, The Johns Hopkins University School of Medicine, MD 21287, USA
| | - Jerry L. Prince
- Dept. of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA
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Murphy OC, Sotirchos ES, Kalaitzidis G, Vasileiou E, Ehrhardt H, Lambe J, Kwakyi O, Nguyen J, Lee AZ, Button J, Dewey BE, Newsome SD, Mowry EM, Fitzgerald KC, Prince JL, Calabresi PA, Saidha S. Trans-Synaptic Degeneration Following Acute Optic Neuritis in Multiple Sclerosis. Ann Neurol 2023; 93:76-87. [PMID: 36218157 PMCID: PMC9933774 DOI: 10.1002/ana.26529] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2021] [Revised: 10/07/2022] [Accepted: 10/07/2022] [Indexed: 02/05/2023]
Abstract
OBJECTIVE To explore longitudinal changes in brain volumetric measures and retinal layer thicknesses following acute optic neuritis (AON) in people with multiple sclerosis (PwMS), to investigate the process of trans-synaptic degeneration, and determine its clinical relevance. METHODS PwMS were recruited within 40 days of AON onset (n = 49), and underwent baseline retinal optical coherence tomography and brain magnetic resonance imaging followed by longitudinal tracking for up to 5 years. A comparator cohort of PwMS without a recent episode of AON were similarly tracked (n = 73). Mixed-effects linear regression models were used. RESULTS Accelerated atrophy of the occipital gray matter (GM), calcarine GM, and thalamus was seen in the AON cohort, as compared with the non-AON cohort (-0.76% vs -0.22% per year [p = 0.01] for occipital GM, -1.83% vs -0.32% per year [p = 0.008] for calcarine GM, -1.17% vs -0.67% per year [p = 0.02] for thalamus), whereas rates of whole-brain, cortical GM, non-occipital cortical GM atrophy, and T2 lesion accumulation did not differ significantly between the cohorts. In the AON cohort, greater AON-induced reduction in ganglion cell+inner plexiform layer thickness over the first year was associated with faster rates of whole-brain (r = 0.32, p = 0.04), white matter (r = 0.32, p = 0.04), and thalamic (r = 0.36, p = 0.02) atrophy over the study period. Significant relationships were identified between faster atrophy of the subcortical GM and thalamus, with worse visual function outcomes after AON. INTERPRETATION These results provide in-vivo evidence for anterograde trans-synaptic degeneration following AON in PwMS, and suggest that trans-synaptic degeneration may be related to clinically-relevant visual outcomes. ANN NEUROL 2023;93:76-87.
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Affiliation(s)
- Olwen C. Murphy
- Division of Neuroimmunology and Neurological Infections, Department of Neurology, Johns Hopkins University, Baltimore, USA
| | - Elias S. Sotirchos
- Division of Neuroimmunology and Neurological Infections, Department of Neurology, Johns Hopkins University, Baltimore, USA
| | - Grigorios Kalaitzidis
- Division of Neuroimmunology and Neurological Infections, Department of Neurology, Johns Hopkins University, Baltimore, USA
| | - Elena Vasileiou
- Division of Neuroimmunology and Neurological Infections, Department of Neurology, Johns Hopkins University, Baltimore, USA
| | - Henrik Ehrhardt
- Division of Neuroimmunology and Neurological Infections, Department of Neurology, Johns Hopkins University, Baltimore, USA
| | - Jeffrey Lambe
- Division of Neuroimmunology and Neurological Infections, Department of Neurology, Johns Hopkins University, Baltimore, USA
| | - Ohemaa Kwakyi
- Division of Neuroimmunology and Neurological Infections, Department of Neurology, Johns Hopkins University, Baltimore, USA
| | - James Nguyen
- Division of Neuroimmunology and Neurological Infections, Department of Neurology, Johns Hopkins University, Baltimore, USA
| | - Alexandra Zambriczki Lee
- Division of Neuroimmunology and Neurological Infections, Department of Neurology, Johns Hopkins University, Baltimore, USA
| | - Julia Button
- Division of Neuroimmunology and Neurological Infections, Department of Neurology, Johns Hopkins University, Baltimore, USA
| | - Blake E. Dewey
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, USA
| | - Scott D. Newsome
- Division of Neuroimmunology and Neurological Infections, Department of Neurology, Johns Hopkins University, Baltimore, USA
| | - Ellen M. Mowry
- Division of Neuroimmunology and Neurological Infections, Department of Neurology, Johns Hopkins University, Baltimore, USA
| | - Kathryn C. Fitzgerald
- Division of Neuroimmunology and Neurological Infections, Department of Neurology, Johns Hopkins University, Baltimore, USA
| | - Jerry L. Prince
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, USA
| | - Peter A. Calabresi
- Division of Neuroimmunology and Neurological Infections, Department of Neurology, Johns Hopkins University, Baltimore, USA
| | - Shiv Saidha
- Division of Neuroimmunology and Neurological Infections, Department of Neurology, Johns Hopkins University, Baltimore, USA
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Vasileiou ES, Hu C, Bernstein CN, Lublin F, Wolinsky JS, Cutter GR, Sotirchos ES, Kowalec K, Salter A, Saidha S, Mowry EM, Calabresi PA, Marrie RA, Fitzgerald KC. Association of Vitamin D Polygenic Risk Scores and Disease Outcome in People With Multiple Sclerosis. NEUROLOGY - NEUROIMMUNOLOGY NEUROINFLAMMATION 2023; 10:10/1/e200062. [DOI: 10.1212/nxi.0000000000200062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/17/2022] [Accepted: 09/26/2022] [Indexed: 11/25/2022]
Abstract
Background and ObjectivesObservational studies suggest low levels of 25-hydroxyvitamin D (25[OH]D) may be associated with increased disease activity in people with multiple sclerosis (PwMS). Large-scale genome-wide association studies (GWAS) suggest 25(OH)D levels are partly genetically determined. The resultant polygenic scores (PGSs) could serve as a proxy for 25(OH)D levels, minimizing potential confounding and reverse causation in analyses with outcomes. Herein, we assess the association of genetically determined 25(OH)D and disease outcomes in MS.MethodsWe generated 25(OH)D PGS for 1,924 PwMS with available genotyping data pooled from 3 studies: the CombiRx trial (n = 575), Johns Hopkins MS Center (n = 1,152), and Immune-Mediated Inflammatory Diseases study (n = 197). 25(OH)D-PGS were derived using summary statistics (p < 5 × 10−8) from a large GWAS including 485,762 individuals with circulating 25(OH)D levels measured. We included clinical and imaging outcomes: Expanded disability status scale (EDSS), timed 25-foot walk (T25FW), nine-hole peg test (9HPT), radiologic activity, and optical coherence tomography-derived ganglion cell inner plexiform layer (GCIPL) thickness. A subset (n = 935) had measured circulating 25(OH)D levels. We fitted multivariable models based on the outcome of interest and pooled results across studies using random effects meta-analysis. Sensitivity analyses included a modifiedpvalue threshold for inclusion in the PGS (5 × 10−5) and applying Mendelian randomization (MR) rather than using PGS.ResultsInitial analyses demonstrated a positive association between generated 25(OH)D-PGS and circulating 25(OH)D levels (per 1SD increase in 25[OH]D PGS: 3.08%, 95% CI: 1.77%, 4.42%;p= 4.33e-06; R2= 2.24%). In analyses with outcomes, we did not observe an association between 25(OH)D-PGS and relapse rate (per 1SD increase in 25[OH]D-PGS: 0.98; 95% CI: 0.87–1.10), EDSS worsening (per 1SD: 1.05; 95% CI: 0.87–1.28), change in T25FW (per 1SD: 0.07%; 95% CI: −0.34 to 0.49), or change in 9HPT (per 1SD: 0.09%; 95% CI: −0.15 to 0.33). 25(OH)D-PGS was not associated with new lesion accrual, lesion volume or other imaging-based outcomes (whole brain, gray, white matter volume loss or GCIPL thinning). The results were similarly null in analyses using otherpvalue thresholds or those applying MR.DiscussionGenetically determined lower 25(OH)D levels were not associated with worse disease outcomes in PwMS and raises questions about the plausibility of a treatment effect of vitamin D in established MS.
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Murphy OC, Kalaitzidis G, Vasileiou E, Filippatou AG, Lambe J, Ehrhardt H, Pellegrini N, Sotirchos ES, Luciano NJ, Liu Y, Fitzgerald KC, Prince JL, Calabresi PA, Saidha S. Optical Coherence Tomography and Optical Coherence Tomography Angiography Findings After Optic Neuritis in Multiple Sclerosis. Front Neurol 2020; 11:618879. [PMID: 33384660 PMCID: PMC7769949 DOI: 10.3389/fneur.2020.618879] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Accepted: 11/24/2020] [Indexed: 12/15/2022] Open
Abstract
Background: In people with multiple sclerosis (MS), optic neuritis (ON) results in inner retinal layer thinning, and reduced density of the retinal microvasculature. Objective: To compare inter-eye differences (IEDs) in macular optical coherence tomography (OCT) and OCT angiography (OCTA) measures in MS patients with a history of unilateral ON (MS ON) vs. MS patients with no history of ON (MS non-ON), and to assess how these measures correlate with visual function outcomes after ON. Methods: In this cross-sectional study, people with MS underwent OCT and OCTA. Superficial vascular plexus (SVP) density of each eye was quantified using a deep neural network. IEDs were calculated with respect to the ON eye in MS ON patients, and with respect to the right eye in MS non-ON patients. Statistical analyses used mixed-effect regression models accounting for intra-subject correlations. Results: We included 43 MS ON patients (with 92 discrete OCT/OCTA visits) and 14 MS non-ON patients (with 24 OCT/OCTA visits). Across the cohorts, mean IED in SVP density was −2.69% (SD 3.23) in MS ON patients, as compared to 0.17% (SD 2.39) in MS non-ON patients (p = 0.002). When the MS ON patients were further stratified according to time from ON and compared to MS non-ON patients with multiple cross-sectional analyses, we identified that IED in SVP density was significantly increased in MS ON patients at 1–3 years (p = < 0.001) and >3 years post-ON (p < 0.001), but not at <3 months (p = 0.21) or 3–12 months post-ON (p = 0.07), while IED in ganglion cell + inner plexiform layer (GCIPL) thickness was significantly increased in MS ON patients at all time points post-ON (p ≦ 0.01 for all). IED in SVP density and IED in GCIPL thickness demonstrated significant relationships with IEDs in 100% contrast, 2.5% contrast, and 1.25% contrast letter acuity in MS ON patients (p < 0.001 for all). Conclusions: Our findings suggest that increased IED in SVP density can be detected after ON in MS using OCTA, and detectable changes in SVP density after ON may occur after changes in GCIPL thickness. IED in SVP density and IED in GCIPL thickness correlate well with visual function outcomes in MS ON patients.
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Affiliation(s)
- Olwen C Murphy
- Division of Neuroimmunology and Neurological Infections, Department of Neurology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Grigorios Kalaitzidis
- Division of Neuroimmunology and Neurological Infections, Department of Neurology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Eleni Vasileiou
- Division of Neuroimmunology and Neurological Infections, Department of Neurology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Angeliki G Filippatou
- Division of Neuroimmunology and Neurological Infections, Department of Neurology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Jeffrey Lambe
- Division of Neuroimmunology and Neurological Infections, Department of Neurology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Henrik Ehrhardt
- Division of Neuroimmunology and Neurological Infections, Department of Neurology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Nicole Pellegrini
- Division of Neuroimmunology and Neurological Infections, Department of Neurology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Elias S Sotirchos
- Division of Neuroimmunology and Neurological Infections, Department of Neurology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Nicholas J Luciano
- Division of Neuroimmunology and Neurological Infections, Department of Neurology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Yihao Liu
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Kathryn C Fitzgerald
- Division of Neuroimmunology and Neurological Infections, Department of Neurology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Jerry L Prince
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, United States
| | - Peter A Calabresi
- Division of Neuroimmunology and Neurological Infections, Department of Neurology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Shiv Saidha
- Division of Neuroimmunology and Neurological Infections, Department of Neurology, Johns Hopkins Hospital, Baltimore, MD, United States
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Ksenofontov SY, Shilyagin PA, Terpelov DA, Gelikonov VM, Gelikonov GV. Numerical method for axial motion artifact correction in retinal spectral-domain optical coherence tomography. FRONTIERS OF OPTOELECTRONICS 2020; 13:393-401. [PMID: 36641561 PMCID: PMC9743928 DOI: 10.1007/s12200-019-0951-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/11/2019] [Accepted: 10/16/2019] [Indexed: 06/12/2023]
Abstract
A numerical method that compensates image distortions caused by random fluctuations of the distance to an object in spectral-domain optical coherence tomography (SD OCT) has been proposed and verified experimentally. The proposed method is based on the analysis of the phase shifts between adjacent scans that are caused by micrometer-scale displacements and the subsequent compensation for the displacements through phase-frequency correction in the spectral space. The efficiency of the method is demonstrated in model experiments with harmonic and random movements of a scattering object as well as during in vivo imaging of the retina of the human eye.
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Affiliation(s)
- Sergey Yu Ksenofontov
- BioMedTech Llc, Nizhny Novgorod, 603155, Russia
- Institute of Applied Physics of the Russian Academy of Science, Nizhny Novgorod, 603950, Russia
| | - Pavel A Shilyagin
- Institute of Applied Physics of the Russian Academy of Science, Nizhny Novgorod, 603950, Russia.
| | - Dmitry A Terpelov
- Institute of Applied Physics of the Russian Academy of Science, Nizhny Novgorod, 603950, Russia
| | - Valentin M Gelikonov
- Institute of Applied Physics of the Russian Academy of Science, Nizhny Novgorod, 603950, Russia
| | - Grigory V Gelikonov
- Institute of Applied Physics of the Russian Academy of Science, Nizhny Novgorod, 603950, Russia
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Murphy OC, Kwakyi O, Iftikhar M, Zafar S, Lambe J, Pellegrini N, Sotirchos ES, Gonzalez-Caldito N, Ogbuokiri E, Filippatou A, Risher H, Cowley N, Feldman S, Fioravante N, Frohman EM, Frohman TC, Balcer LJ, Prince JL, Channa R, Calabresi PA, Saidha S. Alterations in the retinal vasculature occur in multiple sclerosis and exhibit novel correlations with disability and visual function measures. Mult Scler 2020; 26:815-828. [PMID: 31094280 PMCID: PMC6858526 DOI: 10.1177/1352458519845116] [Citation(s) in RCA: 56] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
BACKGROUND The retinal vasculature may be altered in multiple sclerosis (MS), potentially acting as a biomarker of disease processes. OBJECTIVE To compare retinal vascular plexus densities in people with MS (PwMS) and healthy controls (HCs), and examine correlations with visual function and global disability. METHODS In this cross-sectional study, 111 PwMS (201 eyes) and 50 HCs (97 eyes) underwent optical coherence tomography angiography (OCTA). Macular superficial vascular plexus (SVP) and deep vascular plexus (DVP) densities were quantified, and poor quality images were excluded according to an artifact-rating protocol. RESULTS Mean SVP density was 24.1% (SD = 5.5) in MS eyes (26.0% (SD = 4.7) in non-optic neuritis (ON) eyes vs. 21.7% (SD = 5.5) in ON eyes, p < 0.001), as compared to 29.2% (SD = 3.3) in HC eyes (p < 0.001 for all MS eyes and multiple sclerosis optic neuritis (MSON) eyes vs. HC eyes, p = 0.03 for MS non-ON eyes vs. HC eyes). DVP density did not differ between groups. In PwMS, lower SVP density was associated with higher levels of disability (expanded disability status scale (EDSS): R2 = 0.26, p = 0.004; multiple sclerosis functional composite (MSFC): R2 = 0.27, p = 0.03) and lower letter acuity scores (100% contrast: R2 = 0.29; 2.5% contrast: R2 = 0.40; 1.25% contrast: R2 = 0.31; p < 0.001 for all). CONCLUSIONS Retinal SVP density measured by OCTA is reduced across MS eyes, and correlates with visual function, EDSS, and MSFC scores.
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Affiliation(s)
- Olwen C. Murphy
- Division of Neuroimmunology and Neurological Infections,
Department of Neurology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Ohemaa Kwakyi
- Division of Neuroimmunology and Neurological Infections,
Department of Neurology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Mustafa Iftikhar
- Wilmer Eye Institute, Johns Hopkins University School of
Medicine, Baltimore, MD, United States
| | - Sidra Zafar
- Wilmer Eye Institute, Johns Hopkins University School of
Medicine, Baltimore, MD, United States
| | - Jeffrey Lambe
- Division of Neuroimmunology and Neurological Infections,
Department of Neurology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Nicole Pellegrini
- Division of Neuroimmunology and Neurological Infections,
Department of Neurology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Elias S. Sotirchos
- Division of Neuroimmunology and Neurological Infections,
Department of Neurology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Natalia Gonzalez-Caldito
- Division of Neuroimmunology and Neurological Infections,
Department of Neurology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Esther Ogbuokiri
- Division of Neuroimmunology and Neurological Infections,
Department of Neurology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Angeliki Filippatou
- Division of Neuroimmunology and Neurological Infections,
Department of Neurology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Hunter Risher
- Division of Neuroimmunology and Neurological Infections,
Department of Neurology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Norah Cowley
- Division of Neuroimmunology and Neurological Infections,
Department of Neurology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Sydney Feldman
- Division of Neuroimmunology and Neurological Infections,
Department of Neurology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Nicholas Fioravante
- Division of Neuroimmunology and Neurological Infections,
Department of Neurology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Elliot M. Frohman
- Department of Neurology and Ophthalmology, University of
Texas Austin Dell Medical School, Austin, TX, United States
| | - Teresa C. Frohman
- Department of Neurology and Ophthalmology, University of
Texas Austin Dell Medical School, Austin, TX, United States
| | - Laura J. Balcer
- Department of Neurology, New York University Langone
Medical Center, New York, NY, United States
| | - Jerry L. Prince
- Department of Electrical and Computer Engineering, Johns
Hopkins University, Baltimore, MD, United States
| | - Roomasa Channa
- Wilmer Eye Institute, Johns Hopkins University School of
Medicine, Baltimore, MD, United States
- Department of Ophthalmology, Baylor College of Medicine,
Houston, TX, United States
| | - Peter A. Calabresi
- Division of Neuroimmunology and Neurological Infections,
Department of Neurology, Johns Hopkins Hospital, Baltimore, MD, United States
| | - Shiv Saidha
- Division of Neuroimmunology and Neurological Infections,
Department of Neurology, Johns Hopkins Hospital, Baltimore, MD, United States
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Liu Y, Carass A, He Y, Antony BJ, Filippatou A, Saidha S, Solomon SD, Calabresi PA, Prince JL. Layer boundary evolution method for macular OCT layer segmentation. BIOMEDICAL OPTICS EXPRESS 2019; 10:1064-1080. [PMID: 30891330 PMCID: PMC6420297 DOI: 10.1364/boe.10.001064] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2018] [Revised: 12/27/2018] [Accepted: 12/28/2018] [Indexed: 05/30/2023]
Abstract
Optical coherence tomography (OCT) is used to produce high resolution depth images of the retina and is now the standard of care for in-vivo ophthalmological assessment. It is also increasingly being used for evaluation of neurological disorders such as multiple sclerosis (MS). Automatic segmentation methods identify the retinal layers of the macular cube providing consistent results without intra- and inter-rater variation and is faster than manual segmentation. In this paper, we propose a fast multi-layer macular OCT segmentation method based on a fast level set method. Our framework uses contours in an optimized approach specifically for OCT layer segmentation over the whole macular cube. Our algorithm takes boundary probability maps from a trained random forest and iteratively refines the prediction to subvoxel precision. Evaluation on both healthy and multiple sclerosis subjects shows that our method is statistically better than a state-of-the-art graph-based method.
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Affiliation(s)
- Yihao Liu
- 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
- Dept. of Computer Science, The Johns Hopkins University, Baltimore, MD 21218, USA
| | - Yufan He
- Dept. of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA
| | | | - Angeliki Filippatou
- Dept. of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Shiv Saidha
- Dept. of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Sharon D. Solomon
- Wilmer Eye Institute, The Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Peter A. Calabresi
- Dept. of Neurology, The Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
| | - Jerry L. Prince
- Dept. of Electrical and Computer Engineering, The Johns Hopkins University, Baltimore, MD 21218, USA
- Dept. of Computer Science, The Johns Hopkins University, Baltimore, MD 21218, USA
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Lambe J, Murphy OC, Saidha S. Can Optical Coherence Tomography Be Used to Guide Treatment Decisions in Adult or Pediatric Multiple Sclerosis? Curr Treat Options Neurol 2018; 20:9. [DOI: 10.1007/s11940-018-0493-6] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/17/2022]
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Ke B, Mao X, Jiang H, He J, Liu C, Li M, Yuan Y, Wang J. The Relationship Between High-Order Aberration and Anterior Ocular Biometry During Accommodation in Young Healthy Adults. Invest Ophthalmol Vis Sci 2017; 58:5628-5635. [PMID: 29094166 PMCID: PMC5667401 DOI: 10.1167/iovs.17-21712] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023] Open
Abstract
Purpose This study investigated the anterior ocular anatomic origin of high-order aberration (HOA) components using optical coherence tomography and a Shack-Hartmann wavefront sensor. Methods A customized system was built to simultaneously capture images of ocular wavefront aberrations and anterior ocular biometry. Relaxed, 2-diopter (D) and 4-D accommodative states were repeatedly measured in 30 young subjects. Custom software was used to correct optical distortions and measure biometric parameters from the images. Results The anterior ocular biometry changed during 2-D accommodation, in which central lens thickness, ciliary muscle thicknesses at 1 mm posterior to the scleral spur (CMT1), and the maximum value of ciliary muscle thickness increased significantly, whereas anterior chamber depth, CMT3, radius of anterior lens surface curvature (RAL), and radius of posterior lens surface curvature (RPL) decreased significantly. The changes in the anterior ocular parameters during 4-D accommodation were similar to those for the 2-D accommodation. \begin{document}\newcommand{\bialpha}{\boldsymbol{\alpha}}\newcommand{\bibeta}{\boldsymbol{\beta}}\newcommand{\bigamma}{\boldsymbol{\gamma}}\newcommand{\bidelta}{\boldsymbol{\delta}}\newcommand{\bivarepsilon}{\boldsymbol{\varepsilon}}\newcommand{\bizeta}{\boldsymbol{\zeta}}\newcommand{\bieta}{\boldsymbol{\eta}}\newcommand{\bitheta}{\boldsymbol{\theta}}\newcommand{\biiota}{\boldsymbol{\iota}}\newcommand{\bikappa}{\boldsymbol{\kappa}}\newcommand{\bilambda}{\boldsymbol{\lambda}}\newcommand{\bimu}{\boldsymbol{\mu}}\newcommand{\binu}{\boldsymbol{\nu}}\newcommand{\bixi}{\boldsymbol{\xi}}\newcommand{\biomicron}{\boldsymbol{\micron}}\newcommand{\bipi}{\boldsymbol{\pi}}\newcommand{\birho}{\boldsymbol{\rho}}\newcommand{\bisigma}{\boldsymbol{\sigma}}\newcommand{\bitau}{\boldsymbol{\tau}}\newcommand{\biupsilon}{\boldsymbol{\upsilon}}\newcommand{\biphi}{\boldsymbol{\phi}}\newcommand{\bichi}{\boldsymbol{\chi}}\newcommand{\bipsi}{\boldsymbol{\psi}}\newcommand{\biomega}{\boldsymbol{\omega}}\({\rm Z}_4^0\)\end{document} decreased significantly during 2-D accommodation, and \begin{document}\newcommand{\bialpha}{\boldsymbol{\alpha}}\newcommand{\bibeta}{\boldsymbol{\beta}}\newcommand{\bigamma}{\boldsymbol{\gamma}}\newcommand{\bidelta}{\boldsymbol{\delta}}\newcommand{\bivarepsilon}{\boldsymbol{\varepsilon}}\newcommand{\bizeta}{\boldsymbol{\zeta}}\newcommand{\bieta}{\boldsymbol{\eta}}\newcommand{\bitheta}{\boldsymbol{\theta}}\newcommand{\biiota}{\boldsymbol{\iota}}\newcommand{\bikappa}{\boldsymbol{\kappa}}\newcommand{\bilambda}{\boldsymbol{\lambda}}\newcommand{\bimu}{\boldsymbol{\mu}}\newcommand{\binu}{\boldsymbol{\nu}}\newcommand{\bixi}{\boldsymbol{\xi}}\newcommand{\biomicron}{\boldsymbol{\micron}}\newcommand{\bipi}{\boldsymbol{\pi}}\newcommand{\birho}{\boldsymbol{\rho}}\newcommand{\bisigma}{\boldsymbol{\sigma}}\newcommand{\bitau}{\boldsymbol{\tau}}\newcommand{\biupsilon}{\boldsymbol{\upsilon}}\newcommand{\biphi}{\boldsymbol{\phi}}\newcommand{\bichi}{\boldsymbol{\chi}}\newcommand{\bipsi}{\boldsymbol{\psi}}\newcommand{\biomega}{\boldsymbol{\omega}}\({\rm{Z}}_3^{ - 1}\)\end{document}, \begin{document}\newcommand{\bialpha}{\boldsymbol{\alpha}}\newcommand{\bibeta}{\boldsymbol{\beta}}\newcommand{\bigamma}{\boldsymbol{\gamma}}\newcommand{\bidelta}{\boldsymbol{\delta}}\newcommand{\bivarepsilon}{\boldsymbol{\varepsilon}}\newcommand{\bizeta}{\boldsymbol{\zeta}}\newcommand{\bieta}{\boldsymbol{\eta}}\newcommand{\bitheta}{\boldsymbol{\theta}}\newcommand{\biiota}{\boldsymbol{\iota}}\newcommand{\bikappa}{\boldsymbol{\kappa}}\newcommand{\bilambda}{\boldsymbol{\lambda}}\newcommand{\bimu}{\boldsymbol{\mu}}\newcommand{\binu}{\boldsymbol{\nu}}\newcommand{\bixi}{\boldsymbol{\xi}}\newcommand{\biomicron}{\boldsymbol{\micron}}\newcommand{\bipi}{\boldsymbol{\pi}}\newcommand{\birho}{\boldsymbol{\rho}}\newcommand{\bisigma}{\boldsymbol{\sigma}}\newcommand{\bitau}{\boldsymbol{\tau}}\newcommand{\biupsilon}{\boldsymbol{\upsilon}}\newcommand{\biphi}{\boldsymbol{\phi}}\newcommand{\bichi}{\boldsymbol{\chi}}\newcommand{\bipsi}{\boldsymbol{\psi}}\newcommand{\biomega}{\boldsymbol{\omega}}\({\rm{Z}}_3^1\)\end{document}, \begin{document}\newcommand{\bialpha}{\boldsymbol{\alpha}}\newcommand{\bibeta}{\boldsymbol{\beta}}\newcommand{\bigamma}{\boldsymbol{\gamma}}\newcommand{\bidelta}{\boldsymbol{\delta}}\newcommand{\bivarepsilon}{\boldsymbol{\varepsilon}}\newcommand{\bizeta}{\boldsymbol{\zeta}}\newcommand{\bieta}{\boldsymbol{\eta}}\newcommand{\bitheta}{\boldsymbol{\theta}}\newcommand{\biiota}{\boldsymbol{\iota}}\newcommand{\bikappa}{\boldsymbol{\kappa}}\newcommand{\bilambda}{\boldsymbol{\lambda}}\newcommand{\bimu}{\boldsymbol{\mu}}\newcommand{\binu}{\boldsymbol{\nu}}\newcommand{\bixi}{\boldsymbol{\xi}}\newcommand{\biomicron}{\boldsymbol{\micron}}\newcommand{\bipi}{\boldsymbol{\pi}}\newcommand{\birho}{\boldsymbol{\rho}}\newcommand{\bisigma}{\boldsymbol{\sigma}}\newcommand{\bitau}{\boldsymbol{\tau}}\newcommand{\biupsilon}{\boldsymbol{\upsilon}}\newcommand{\biphi}{\boldsymbol{\phi}}\newcommand{\bichi}{\boldsymbol{\chi}}\newcommand{\bipsi}{\boldsymbol{\psi}}\newcommand{\biomega}{\boldsymbol{\omega}}\({\rm{Z}}_4^0\)\end{document}, and \begin{document}\newcommand{\bialpha}{\boldsymbol{\alpha}}\newcommand{\bibeta}{\boldsymbol{\beta}}\newcommand{\bigamma}{\boldsymbol{\gamma}}\newcommand{\bidelta}{\boldsymbol{\delta}}\newcommand{\bivarepsilon}{\boldsymbol{\varepsilon}}\newcommand{\bizeta}{\boldsymbol{\zeta}}\newcommand{\bieta}{\boldsymbol{\eta}}\newcommand{\bitheta}{\boldsymbol{\theta}}\newcommand{\biiota}{\boldsymbol{\iota}}\newcommand{\bikappa}{\boldsymbol{\kappa}}\newcommand{\bilambda}{\boldsymbol{\lambda}}\newcommand{\bimu}{\boldsymbol{\mu}}\newcommand{\binu}{\boldsymbol{\nu}}\newcommand{\bixi}{\boldsymbol{\xi}}\newcommand{\biomicron}{\boldsymbol{\micron}}\newcommand{\bipi}{\boldsymbol{\pi}}\newcommand{\birho}{\boldsymbol{\rho}}\newcommand{\bisigma}{\boldsymbol{\sigma}}\newcommand{\bitau}{\boldsymbol{\tau}}\newcommand{\biupsilon}{\boldsymbol{\upsilon}}\newcommand{\biphi}{\boldsymbol{\phi}}\newcommand{\bichi}{\boldsymbol{\chi}}\newcommand{\bipsi}{\boldsymbol{\psi}}\newcommand{\biomega}{\boldsymbol{\omega}}\({\rm{Z}}_6^0\)\end{document} shifted to negative values during 4-D accommodation. The change in \begin{document}\newcommand{\bialpha}{\boldsymbol{\alpha}}\newcommand{\bibeta}{\boldsymbol{\beta}}\newcommand{\bigamma}{\boldsymbol{\gamma}}\newcommand{\bidelta}{\boldsymbol{\delta}}\newcommand{\bivarepsilon}{\boldsymbol{\varepsilon}}\newcommand{\bizeta}{\boldsymbol{\zeta}}\newcommand{\bieta}{\boldsymbol{\eta}}\newcommand{\bitheta}{\boldsymbol{\theta}}\newcommand{\biiota}{\boldsymbol{\iota}}\newcommand{\bikappa}{\boldsymbol{\kappa}}\newcommand{\bilambda}{\boldsymbol{\lambda}}\newcommand{\bimu}{\boldsymbol{\mu}}\newcommand{\binu}{\boldsymbol{\nu}}\newcommand{\bixi}{\boldsymbol{\xi}}\newcommand{\biomicron}{\boldsymbol{\micron}}\newcommand{\bipi}{\boldsymbol{\pi}}\newcommand{\birho}{\boldsymbol{\rho}}\newcommand{\bisigma}{\boldsymbol{\sigma}}\newcommand{\bitau}{\boldsymbol{\tau}}\newcommand{\biupsilon}{\boldsymbol{\upsilon}}\newcommand{\biphi}{\boldsymbol{\phi}}\newcommand{\bichi}{\boldsymbol{\chi}}\newcommand{\bipsi}{\boldsymbol{\psi}}\newcommand{\biomega}{\boldsymbol{\omega}}\({\rm{Z}}_4^0\)\end{document} negatively correlated with those in CMT1, and the negative change in \begin{document}\newcommand{\bialpha}{\boldsymbol{\alpha}}\newcommand{\bibeta}{\boldsymbol{\beta}}\newcommand{\bigamma}{\boldsymbol{\gamma}}\newcommand{\bidelta}{\boldsymbol{\delta}}\newcommand{\bivarepsilon}{\boldsymbol{\varepsilon}}\newcommand{\bizeta}{\boldsymbol{\zeta}}\newcommand{\bieta}{\boldsymbol{\eta}}\newcommand{\bitheta}{\boldsymbol{\theta}}\newcommand{\biiota}{\boldsymbol{\iota}}\newcommand{\bikappa}{\boldsymbol{\kappa}}\newcommand{\bilambda}{\boldsymbol{\lambda}}\newcommand{\bimu}{\boldsymbol{\mu}}\newcommand{\binu}{\boldsymbol{\nu}}\newcommand{\bixi}{\boldsymbol{\xi}}\newcommand{\biomicron}{\boldsymbol{\micron}}\newcommand{\bipi}{\boldsymbol{\pi}}\newcommand{\birho}{\boldsymbol{\rho}}\newcommand{\bisigma}{\boldsymbol{\sigma}}\newcommand{\bitau}{\boldsymbol{\tau}}\newcommand{\biupsilon}{\boldsymbol{\upsilon}}\newcommand{\biphi}{\boldsymbol{\phi}}\newcommand{\bichi}{\boldsymbol{\chi}}\newcommand{\bipsi}{\boldsymbol{\psi}}\newcommand{\biomega}{\boldsymbol{\omega}}\({\rm{Z}}_3^1\)\end{document} correlated with changes in RAL and CMT1. Conclusions HOA components altered during step-controlled accommodative stimuli. Ciliary muscle first contracted during stepwise accommodation, which may directly contribute to the reduction of spherical aberration (SA). The lens morphology was then altered, and the change in anterior lens surface curvature was related to the variation of coma.
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Affiliation(s)
- Bilian Ke
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.,Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, Florida, United States
| | - Xinjie Mao
- Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, Florida, United States.,School of Ophthalmology and Optometry, Wenzhou Medical University, Wenzhou, China
| | - Hong Jiang
- Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, Florida, United States
| | - Jichang He
- New England College of Optometry, Boston, Massachusetts, United States
| | - Che Liu
- Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, Florida, United States
| | - Min Li
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Ying Yuan
- Department of Ophthalmology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Jianhua Wang
- Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, Florida, United States
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10
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Schachar RA, Mani M, Schachar IH. Image registration reveals central lens thickness minimally increases during accommodation. Clin Ophthalmol 2017; 11:1625-1636. [PMID: 28979092 PMCID: PMC5602687 DOI: 10.2147/opth.s144238] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022] Open
Abstract
Purpose To evaluate anterior chamber depth, central crystalline lens thickness and lens curvature during accommodation. Setting California Retina Associates, El Centro, CA, USA. Design Healthy volunteer, prospective, clinical research swept-source optical coherence biometric image registration study of accommodation. Methods Ten subjects (4 females and 6 males) with an average age of 22.5 years (range: 20–26 years) participated in the study. A 45° beam splitter attached to a Zeiss IOLMaster 700 (Carl Zeiss Meditec Inc., Jena, Germany) biometer enabled simultaneous imaging of the cornea, anterior chamber, entire central crystalline lens and fovea in the dilated right eyes of subjects before, and during focus on a target 11 cm from the cornea. Images with superimposable foveal images, obtained before and during accommodation, that met all of the predetermined alignment criteria were selected for comparison. This registration requirement assured that changes in anterior chamber depth and central lens thickness could be accurately and reliably measured. The lens radii of curvatures were measured with a pixel stick circle. Results Images from only 3 of 10 subjects met the predetermined criteria for registration. Mean anterior chamber depth decreased, −67 μm (range: −0.40 to −110 μm), and mean central lens thickness increased, 117 μm (range: 100–130 μm). The lens surfaces steepened, anterior greater than posterior, while the lens, itself, did not move or shift its position as appeared from the lack of movement of the lens nucleus, during 7.8 diopters of accommodation, (range: 6.6–9.7 diopters). Conclusion Image registration, with stable invariant references for image correspondence, reveals that during accommodation a large increase in lens surface curvatures is associated with only a small increase in central lens thickness and no change in lens position.
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Affiliation(s)
- Ronald A Schachar
- Department of Physics, University of Texas at Arlington, Arlington, TX
| | | | - Ira H Schachar
- Byers Eye Institute of Stanford University, Palo Alto, CA, USA
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11
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Antony BJ, Carass A, Lang A, Kim BJ, Zack DJ, Prince JL. Longitudinal Analysis of Mouse SDOCT Volumes. ACTA ACUST UNITED AC 2017; 10137. [PMID: 29138527 DOI: 10.1117/12.2257432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Spectral-domain optical coherence tomography (SDOCT), in addition to its routine clinical use in the diagnosis of ocular diseases, has begun to find increasing use in animal studies. Animal models are frequently used to study disease mechanisms as well as to test drug efficacy. In particular, SDOCT provides the ability to study animals longitudinally and non-invasively over long periods of time. However, the lack of anatomical landmarks makes the longitudinal scan acquisition prone to inconsistencies in orientation. Here, we propose a method for the automated registration of mouse SDOCT volumes. The method begins by accurately segmenting the blood vessels and the optic nerve head region in the scans using a pixel classification approach. The segmented vessel maps from follow-up scans were registered using an iterative closest point (ICP) algorithm to the baseline scan to allow for the accurate longitudinal tracking of thickness changes. Eighteen SDOCT volumes from a light damage model study were used to train a random forest utilized in the pixel classification step. The area under the curve (AUC) in a leave-one-out study for the retinal blood vessels and the optic nerve head (ONH) was found to be 0.93 and 0.98, respectively. The complete proposed framework, the retinal vasculature segmentation and the ICP registration, was applied to a secondary set of scans obtained from a light damage model. A qualitative assessment of the registration showed no registration failures.
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Affiliation(s)
- Bhavna J Antony
- Department of Electrical and Computer Engineering, Johns Hopkins University
| | - Aaron Carass
- Department of Electrical and Computer Engineering, Johns Hopkins University
| | - Andrew Lang
- Department of Electrical and Computer Engineering, Johns Hopkins University
| | - Byung-Jin Kim
- Wilmer Eye Institute, Johns Hopkins University School of Medicine
| | - Donald J Zack
- Wilmer Eye Institute, Johns Hopkins University School of Medicine
| | - Jerry L Prince
- Department of Electrical and Computer Engineering, Johns Hopkins University
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