Objektive Analyse von Hornhautnerven und dendritischen Zellen

Philipp Steven; Asif Setu

doi:10.1055/a-2307-0313

Klinische Monatsblätter für Augenheilkunde, Inhaltsverzeichnis

Klin Monbl Augenheilkd 2024; 241(06): 713-721
DOI: 10.1055/a-2307-0313

Übersicht

Objektive Analyse von Hornhautnerven und dendritischen Zellen

Artikel in mehreren Sprachen: deutsch | English

Philipp Steven

¹Klinik I für Innere Medizin, Centrum für Integrierte Onkologie CIO, Uniklinik Köln, Deutschland

²Zentrum für Augenheilkunde, AG Augenoberfläche, Uniklinik Köln, Deutschland

,

Asif Setu

²Zentrum für Augenheilkunde, AG Augenoberfläche, Uniklinik Köln, Deutschland

› Institutsangaben

Abstract

Zusammenfassung

Hornhautnerven und dendritische Zellen werden zunehmend bei der Diagnostik von Erkrankungen der Augenoberfläche als klinische Parameter mittels intravitaler Konfokalmikroskopie dargestellt. In dieser Übersichtsarbeit werden unterschiedliche Verfahren der Bildauswertung dargestellt. Die Verwendung von Deep-Learning-Algorithmen, die eine automatisierte Mustererkennung ermöglichen, wird anhand eigener Entwicklungen detailliert erläutert und mit anderen etablierten Verfahren verglichen.

Schlüsselwörter

Konfokalmikroskopie - Hornhautnerven - dendritische Zellen - Deep Learning

Volltext

Referenzen

References/Literatur
1 Lemp MA, Dilly PN, Boyde A. Tandem-scanning (confocal) microscopy of the full-thickness cornea. Cornea 1985; 4: 205-209
2 Cavanagh HD, Jester JV, Essepian J. et al. Confocal microscopy of the living eye. CLAO J 1990; 16: 65-73
3 Jester JV, Andrews PM, Petroll WM. et al. In vivo, real-time confocal imaging. J Electron Microsc Tech 1991; 18: 50-60
4 Bailly N, Sherif ZA, Pleyer U. et al. [Confocal microscopy in corneal dystrophies: a comparison between confocal slit scanning (ConfoScan P2) and laser scanning microscopy (Rostock Cornea Modul-HRT II)]. Klin Monbl Augenheilkd 2006; 223: 735-742
5 Stave J, Slowik C, Somodi S. et al. [Keratinocyte density of the cornea in vivo. Automated measurement with a modified confocal microscopy MICROPHTHAL]. Klin Monbl Augenheilkd 1998; 213: 38-44
6 Stave J, Guthoff R. [Imaging the tear film and in vivo cornea. Initial results with a modified confocal laser scanning ophthalmoscope]. Ophthalmologe 1998; 95: 104-109
7 Stave J, Zinser G, Grummer G. et al. [Modified Heidelberg Retinal Tomograph HRT. Initial results of in vivo presentation of corneal structures]. Ophthalmologe 2002; 99: 276-280
8 Labbe A, Khammari C, Dupas B. et al. Contribution of in vivo confocal microscopy to the diagnosis and management of infectious keratitis. Ocul Surf 2009; 7: 41-52
9 Schlemm T. Nerven der Cornea. Ammon. Z Ophthalmol 1831; (01) 113-114
10 Auran JD, Koester CJ, Kleiman NJ. et al. Scanning slit confocal microscopic observation of cell morphology and movement within the normal human anterior cornea. Ophthalmology 1995; 102: 33-41
11 Shaheen BS, Bakir M, Jain S. Corneal nerves in health and disease. Surv Ophthalmol 2014; 59: 263-285
12 Liu J, Li Z. Resident Innate Immune Cells in the Cornea. Front Immunol 2021; 12: 620284
13 Matsumoto Y, Ibrahim OMA. Application of In Vivo Confocal Microscopy in Dry Eye Disease. Invest Ophthalmol Vis Sci 2018; 59: DES41-DES47
14 Uchino Y, Uchino M, Mizuno M. et al. Morphological alterations in corneal nerves of patients with dry eye and associated biomarkers. Exp Eye Res 2023; 230: 109438
15 Cox SM, Kheirkhah A, Aggarwal S. et al. Alterations in corneal nerves in different subtypes of dry eye disease: An in vivo confocal microscopy study. Ocul Surf 2021; 22: 135-142
16 Bucher F, Hos D, Matthaei M. et al. Corneal nerve alterations after Descemet membrane endothelial keratoplasty: an in vivo confocal microscopy study. Cornea 2014; 33: 1134-1139
17 Liu YC, Jung ASJ, Chin JY. et al. Cross-sectional Study on Corneal Denervation in Contralateral Eyes Following SMILE Versus LASIK. J Refract Surg 2020; 36: 653-660
18 Li M, Wei R, Yang W. et al. Femtosecond Laser-Assisted Allogenic Lenticule Implantation for Corneal Ectasia After LASIK: A 3-Year In Vivo Confocal Microscopic Investigation. J Refract Surg 2020; 36: 714-722
19 Chao C, Zhou S, Stapleton F. et al. The structural and functional corneal reinnervation mechanism at different regions after LASIK-an in vivo confocal microscopy study. Graefes Arch Clin Exp Ophthalmol 2022; 260: 163-172
20 Serramito-Blanco M, Canadas P, Villa-Collar C. et al. Corneal Confocal Analysis after Scleral Lens Wear in Keratoconus Patients: A Pilot Study. Optom Vis Sci 2022; 99: 800-806
21 Patel SV, McLaren JW, Hodge DO. et al. Confocal microscopy in vivo in corneas of long-term contact lens wearers. Invest Ophthalmol Vis Sci 2002; 43: 995-1003
22 Leonardi A, Feuerman OM, Salami E. et al. Coexistence of neuropathic corneal pain, corneal nerve abnormalities, depression, and low quality of life. Eye (Lond) 2024; 38: 499-506
23 Kalteniece A, Ferdousi M, Azmi S. et al. Corneal nerve loss is related to the severity of painful diabetic neuropathy. Eur J Neurol 2022; 29: 286-294
24 Badian RA, Ekman L, Pripp AH. et al. Comparison of Novel Wide-Field In Vivo Corneal Confocal Microscopy With Skin Biopsy for Assessing Peripheral Neuropathy in Type 2 Diabetes. Diabetes 2023; 72: 908-917
25 Keskiner-Ozturk E, Akkaya-Turhan S, Toker E. et al. Corneal nerve fiber involvement in chronic inflammatory demyelinating polyneuropathy. Neurol Sci 2023; 44: 2509-2516
26 Roszkowska AM, Wylegala A, Gargiulo L. et al. Corneal Sub-Basal Nerve Plexus in Non-Diabetic Small Fiber Polyneuropathies and the Diagnostic Role of In Vivo Corneal Confocal Microscopy. J Clin Med 2023; 12: 664
27 Guldiken YC, Petropoulos IN, Malik A. et al. Corneal confocal microscopy identifies corneal nerve fiber loss in patients with migraine. Cephalalgia 2023; 43: 3331024231170810
28 Petropoulos IN, Bitirgen G, Ferdousi M. et al. Corneal Confocal Microscopy to Image Small Nerve Fiber Degeneration: Ophthalmology Meets Neurology. Front Pain Res (Lausanne) 2021; 2: 725363
29 Oudejans LC, Niesters M, Brines M. et al. Quantification of small fiber pathology in patients with sarcoidosis and chronic pain using cornea confocal microscopy and skin biopsies. J Pain Res 2017; 10: 2057-2065
30 Moein HR, Akhlaq A, Dieckmann G. et al. Visualization of microneuromas by using in vivo confocal microscopy: An objective biomarker for the diagnosis of neuropathic corneal pain?. Ocul Surf 2020; 18: 651-656
31 Villani E, Galimberti D, Viola F. et al. Corneal involvement in rheumatoid arthritis: an in vivo confocal study. Invest Ophthalmol Vis Sci 2008; 49: 560-564
32 Chiang JCB, Goldstein D, Tavakoli A. et al. Corneal dendritic cells and the subbasal nerve plexus following neurotoxic treatment with oxaliplatin or paclitaxel. Sci Rep 2021; 11: 22884
33 Akhlaq A, Colon C, Cavalcanti BM. et al. Density and distribution of dendritiform cells in the peripheral cornea of healthy subjects using in vivo confocal microscopy. Ocul Surf 2022; 26: 157-165
34 Cruzat A, Qazi Y, Hamrah P. In Vivo Confocal Microscopy of Corneal Nerves in Health and Disease. Ocul Surf 2017; 15: 15-47
35 Patel DV, McGhee CN. In vivo confocal microscopy of human corneal nerves in health, in ocular and systemic disease, and following corneal surgery: a review. Br J Ophthalmol 2009; 93: 853-860
36 Dabbah MA, Graham J, Petropoulos IN. et al. Automatic analysis of diabetic peripheral neuropathy using multi-scale quantitative morphology of nerve fibres in corneal confocal microscopy imaging. Med Image Anal 2011; 15: 738-747
37 Chen X, Graham J, Dabbah MA. et al. An automatic tool for quantification of nerve fibers in corneal confocal microscopy images. IEEE Trans Biomed Eng 2017; 64: 786-794
38 Scarpa F, Grisan E, Ruggeri A. Automatic recognition of corneal nerve structures in images from confocal microscopy. Invest Ophthalmol Vis Sci 2008; 49: 4801-4807
39 Kim J, Markoulli M. Automatic analysis of corneal nerves imaged using in vivo confocal microscopy. Clin Exp Optom 2018; 101: 147-161
40 Dabbah MA, Graham J, Petropoulos I. et al. Dual-model automatic detection of nerve-fibres in corneal confocal microscopy images. Med Image Comput Comput Assist Interv 2010; 13: 300-307
41 Ting DSW, Peng L, Varadarajan AV. et al. Deep learning in ophthalmology: The technical and clinical considerations. Prog Retin Eye Res 2019; 72: 100759
42 Lundervold AS, Lundervold A. An overview of deep learning in medical imaging focusing on MRI. Z Med Phys 2019; 29: 102-127
43 Lecun Y, Bengio Y, Hinton G. Deep learning. Nature 2015; 521: 436-444
44 Shen D, Wu G. Suk H Il. Deep Learning in Medical Image Analysis. Annu Rev Biomed Eng 2017; 19: 221-248
45 Jiang Z, Zhang H, Wang Y. et al. Retinal blood vessel segmentation using fully convolutional network with transfer learning. Comput Med Imaging Graph 2018; 68: 1-15
46 Mishra Z, Ganegoda A, Selicha J. et al. Automated Retinal Layer Segmentation Using Graph-based Algorithm Incorporating Deep-learning-derived Information. Sci Rep 2020; 10: 1-8
47 Sreng S, Maneerat N, Hamamoto K. et al. Deep Learning for Optic Disc Segmentation and Glaucoma Diagnosis on Retinal Images. Appl Sci 2020; 10: 4916
48 Fu H, Baskaran M, Xu Y. et al. A Deep Learning System for Automated Angle-Closure Detection in Anterior Segment Optical Coherence Tomography Images. Am J Ophthalmol 2019; 203: 37-45
49 Setu MAKMAK, Horstmann J, Schmidt S. et al. Deep learning-based automatic meibomian gland segmentation and morphology assessment in infrared meibography. Sci Rep 2021; 11: 7649
50 Williams BM, Borroni D, Liu R. et al. An artificial intelligence-based deep learning algorithm for the diagnosis of diabetic neuropathy using corneal confocal microscopy: a development and validation study. Diabetologia 2020; 63: 419-430
51 Wei S, Shi F, Wang Y. et al. A Deep Learning Model for Automated Sub-Basal Corneal Nerve Segmentation and Evaluation Using In Vivo Confocal Microscopy. Transl Vis Sci Technol 2020; 9: 32
52 Colonna A, Scarpa F, Ruggeri A. Segmentation of Corneal Nerves Using a U-Net-Based Convolutional Neural Network. In: Stoyanov D. et al. Computational Pathology and Ophthalmic Medical Image Analysis. OMIA COMPAY 2018 2018. Lecture Notes in Computer Science, vol 11039. Cham: Springer; 2018: 185-192
53 Oakley JD, Russakoff DB, McCarron ME. et al. Deep learning-based analysis of macaque corneal sub-basal nerve fibers in confocal microscopy images. Eye Vis (Lond) 2020; 7: 27
54 Yıldız E, Arslan AT, Taş AY. et al. Generative Adversarial Network Based Automatic Segmentation of Corneal Subbasal Nerves on In Vivo Confocal Microscopy Images. Transl Vis Sci Technol 2021; 10: 33
55 Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. In: Navab N, Hornegger J, Wells W, Frangi A. eds. Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science, vol 9351. Cham: Springer; 2015: 234-241
56 Zhu W, Huang Y, Zeng L. et al. AnatomyNet: Deep learning for fast and fully automated whole-volume segmentation of head and neck anatomy. Med Phys 2019; 46: 576-589
57 Nasrullah N, Sang J, Alam MS. et al. Automated lung nodule detection and classification using deep learning combined with multiple strategies. Sensors (Basel) 2019; 19: 3722
58 Norman B, Pedoia V, Majumdar S. Use of 2D U-net convolutional neural networks for automated cartilage and meniscus segmentation of knee MR imaging data to determine relaxometry and morphometry. Radiology 2018; 288: 177-185
59 Setu MAK, Schmidt S, Musial G. et al. Segmentation and Evaluation of Corneal Nerves and Dendritic Cells From In Vivo Confocal Microscopy Images Using Deep Learning. Transl Vis Sci Technol 2022; 11: 24
60 He K, Gkioxari G, Dollar P, Girshick R. Mask R-CNN. 2017 IEEE International Conference on Computer Vision (ICCV), Venice, Italy. Institute of Electrical and Electronics Engineers Inc.; 2017: 2980-2988
61 He K, Zhang X, Ren S, Sun J. Deep Residual Learning for Image Recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, NV, USA: 2016: 770-778
62 Lin TY, Maire M, Belongie S. et al. Microsoft COCO: Common objects in context. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8693 LNCS. Cham: Springer; 2014: 740-755