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Chiang JCB, Roy M, Kim J, Markoulli M, Krishnan AV. In-vivo corneal confocal microscopy: Imaging analysis, biological insights and future directions. Commun Biol 2023; 6:652. [PMID: 37336941 DOI: 10.1038/s42003-023-05005-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Accepted: 05/31/2023] [Indexed: 06/21/2023] Open
Abstract
In-vivo corneal confocal microscopy is a powerful imaging technique which provides clinicians and researcher with the capabilities to observe microstructures at the ocular surfaces in significant detail. In this Mini Review, the optics and image analysis methods with the use of corneal confocal microscopy are discussed. While novel insights of neuroanatomy and biology of the eyes, particularly the ocular surface, have been provided by corneal confocal microscopy, some debatable elements observed using this technique remain and these are explored in this Mini Review. Potential improvements in imaging methodology and instrumentation are also suggested.
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Affiliation(s)
- Jeremy Chung Bo Chiang
- School of Optometry and Vision Science, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia
- School of Optometry, College of Health and Life Sciences, Aston University, Birmingham, NSW, UK
| | - Maitreyee Roy
- School of Optometry and Vision Science, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia
| | - Juno Kim
- School of Optometry and Vision Science, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia
| | - Maria Markoulli
- School of Optometry and Vision Science, Faculty of Medicine and Health, University of New South Wales, Sydney, NSW, Australia
| | - Arun V Krishnan
- School of Clinical Medicine, University of New South Wales, Sydney, NSW, Australia.
- Department of Neurology, Prince of Wales Hospital, Sydney, NSW, Australia.
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Cosmo E, Midena G, Frizziero L, Bruno M, Cecere M, Midena E. Corneal Confocal Microscopy as a Quantitative Imaging Biomarker of Diabetic Peripheral Neuropathy: A Review. J Clin Med 2022; 11:jcm11175130. [PMID: 36079060 PMCID: PMC9457345 DOI: 10.3390/jcm11175130] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 08/26/2022] [Accepted: 08/29/2022] [Indexed: 11/16/2022] Open
Abstract
Distal symmetric polyneuropathy (DPN), particularly chronic sensorimotor DPN, represents one of the most frequent complications of diabetes, affecting 50% of diabetic patients and causing an enormous financial burden. Whilst diagnostic methods exist to detect and monitor this condition, they have significant limitations, mainly due to their high subjectivity, invasiveness, and non-repeatability. Corneal confocal microscopy (CCM) is an in vivo, non-invasive, and reproducible diagnostic technique for the study of all corneal layers including the sub-basal nerve plexus, which represents part of the peripheral nervous system. We reviewed the current literature on the use of CCM as an instrument in the assessment of diabetic patients, particularly focusing on its role in the study of sub-basal nerve plexus alterations as a marker of DPN. CCM has been demonstrated to be a valid in vivo tool to detect early sub-basal nerve plexus damage in adult and pediatric diabetic patients, correlating with the severity of DPN. Despite its great potential, CCM has still limited application in daily clinical practice, and more efforts still need to be made to allow the dissemination of this technique among doctors taking care of diabetic patients.
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Affiliation(s)
| | | | - Luisa Frizziero
- Department of Neuroscience-Ophthalmology, University of Padova, 35128 Padova, Italy
| | | | | | - Edoardo Midena
- IRCCS—Fondazione Bietti, 00198 Rome, Italy
- Department of Neuroscience-Ophthalmology, University of Padova, 35128 Padova, Italy
- Correspondence: ; Tel.: +39-049-821-2110
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Chen Z, Yin X, Lin L, Shi G, Mo J. Centerline extraction by neighborhood-statistics thinning for quantitative analysis of corneal nerve fibers. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac7b63] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2022] [Accepted: 06/22/2022] [Indexed: 11/11/2022]
Abstract
Abstract
Objective. Corneal nerve fiber (CNF) has been found to exhibit morphological changes associated with various diseases, which can therefore be utilized to aid in the early diagnosis of those diseases. CNF is usually visualized under corneal confocal microscopy (CCM) in clinic. To obtain the diagnostic biomarkers from CNF image produced from CCM, image processing and quantitative analysis are needed. Usually, CNF is segmented first and then CNF’s centerline is extracted, allowing for measuring geometrical and topological biomarkers of CNF, such as density, tortuosity, and length. Consequently, the accuracy of the segmentation and centerline extraction can make a big impact on the biomarker measurement. Thus, this study is aimed to improve the accuracy and universality of centerline extraction. Approach. We developed a new thinning algorithm based on neighborhood statistics, called neighborhood-statistics thinning (NST), to extract the centerline of CNF. Compared with traditional thinning and skeletonization techniques, NST exhibits a better capability to preserve the fine structure of CNF which can effectively benefit the biomarkers measurement above. Moreover, NST incorporates a fitting process, which can make centerline extraction be less influenced by image segmentation. Main results. This new method is evaluated on three datasets which are segmented with five different deep learning networks. The results show that NST is superior to thinning and skeletonization on all the CNF-segmented datasets with a precision rate above 0.82. Last, NST is attempted to be applied for the diagnosis of keratitis with the quantitative biomarkers measured from the extracted centerlines. Longer length and higher density but lower tortuosity were found on the CNF of keratitis patients as compared to healthy patients. Significance. This demonstrates that NST has a good potential to aid in the diagnostics of eye diseases in clinic.
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Klisser J, Tummanapalli SS, Kim J, Chiang JCB, Khou V, Issar T, Naduvilath T, Poynten AM, Markoulli M, Krishnan AV. Automated analysis of corneal nerve tortuosity in diabetes: implications for neuropathy detection. Clin Exp Optom 2022; 105:487-493. [DOI: 10.1080/08164622.2021.1940875] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
Affiliation(s)
- Jacob Klisser
- Prince of Wales Clinical School, University of New South Wales, Sydney, Australia
| | | | - Juno Kim
- School of Optometry & Vision Science, University of New South Wales, Sydney, Australia
| | | | - Vincent Khou
- School of Optometry & Vision Science, University of New South Wales, Sydney, Australia
- Centre for Eye Health, University of New South Wales, Sydney, Australia
| | - Tushar Issar
- Prince of Wales Clinical School, University of New South Wales, Sydney, Australia
| | - Thomas Naduvilath
- School of Optometry & Vision Science, University of New South Wales, Sydney, Australia
| | - Ann M Poynten
- Department of Endocrinology, Prince of Wales Hospital, Sydney, Australia
| | - Maria Markoulli
- School of Optometry & Vision Science, University of New South Wales, Sydney, Australia
| | - Arun V Krishnan
- Prince of Wales Clinical School, University of New South Wales, Sydney, Australia
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Su P, Chen T, Xie J, Zheng Y, Qi H, Borroni D, Zhao Y, Liu J. Corneal nerve tortuosity grading via ordered weighted averaging-based feature extraction. Med Phys 2020; 47:4983-4996. [PMID: 32761618 DOI: 10.1002/mp.14431] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2020] [Revised: 07/20/2020] [Accepted: 07/21/2020] [Indexed: 01/26/2023] Open
Abstract
PURPOSE Tortuosity of corneal nerve fibers acquired by in vivo Confocal Microscopy (IVCM) are closely correlated to numerous diseases. While tortuosity assessment has conventionally been conducted through labor-intensive manual evaluation, this warrants an automated and objective tortuosity assessment of curvilinear structures. This paper proposes a method that extracts the image-level features for corneal nerve tortuosity grading. METHODS For an IVCM image, all corneal nerve fibers are first segmented and then, their tortuosity are calculated by morphological measures. The ordered weighted averaging (OWA) approach, and the k-Nearest-Neighbor guided dependent ordered weighted averaging (kNNDOWA) approach are proposed to aggregate the tortuosity values and form a set of extracted features. This is followed by running the Wrapper method, a supervised feature selection, with an aim to identify the most informative attributes for tortuosity grading. RESULTS Validated on a public and an in-house benchmark data sets, experimental results demonstrate superiority of the proposed method over the conventional averaging and length-weighted averaging methods with performance gain in accuracy (15.44% and 14.34%, respectively). CONCLUSIONS The simultaneous use of multiple aggregation operators could extract the image-level features that lead to more stable and robust results compared with that using average and length-weighted average. The OWA method could facilitate the explanation of derived aggregation behavior through stress functions. The kNNDOWA method could mitigate the effects of outliers in the image-level feature extraction.
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Affiliation(s)
- Pan Su
- Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315300, China.,School of Control and Computer Engineering, North China Electric Power University, Baoding, 071003, China
| | - Tianhua Chen
- School of Computing and Engineering, University of Huddersfield, Huddersfield, HD1 3DH, UK
| | - Jianyang Xie
- Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315300, China
| | - Yalin Zheng
- Department of Eye and Vision Science, University of Liverpool, Liverpool, L69 3BX, UK
| | - Hong Qi
- Department of Ophthalmology, Peking University Third Hospital, Beijing, 100191, China
| | - Davide Borroni
- St. Paul's Eye Unit, Royal Liverpool University Hospital, Liverpool, L69 3BX, UK
| | - Yitian Zhao
- Cixi Institute of Biomedical Engineering, Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, 315300, China
| | - Jiang Liu
- Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, 518055, China
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