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ODTiD: Optic Nerve Head SD-OCT Image Dataset

Authors Jothi Balaji J , Lakshminarayanan V

Received 1 September 2021

Accepted for publication 11 October 2021

Published 21 October 2021 Volume 2021:15 Pages 4239—4245

DOI https://doi.org/10.2147/OPTH.S337174

Checked for plagiarism Yes

Review by Single anonymous peer review

Peer reviewer comments 2

Editor who approved publication: Dr Scott Fraser

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Janarthanam Jothi Balaji,1 Vasudevan Lakshminarayanan2

1Department of Optometry, Medical Research Foundation, Chennai, Tamil Nadu, 600006, India; 2Theoretical and Experimental Epistemology Lab, School of Optometry and Vision Science, University of Waterloo, Waterloo, Ontario, N2L 3G1, Canada

Correspondence: Janarthanam Jothi Balaji
Department of Optometry, Medical Research Foundation, Chennai, Tamil Nadu, 600006, India
Tel +91-44-4227-1500
Fax +91-44-2825-4180
Email [email protected]

Introduction: Optic disc tilt (ODT) or tilted optic disc is a common finding in the general population. It is due to anomalous development caused by the malclosure of the embryonic optic fissure. ODT is commonly associated with high myopia as well as other conditions. In recent days, the common method to image the optic disc (OD) is by optical coherence tomography (OCT). To the best of our knowledge, there are no datasets of ODT available in the public domain. This dataset aims to make open access raw ODT OCT images to test out new image processing segmentation algorithms.
Methods: This dataset of ODT images contains both horizontal and vertical cross-sectional images obtained using spectral-domain optical coherence tomography (SD-OCT, Cirrus 5000, Carl Zeiss Meditec Inc., Dublin, CA). The optic disc cube 200× 200 program was used and all the images are aligned with the center of the optic nerve head. This dataset includes images from both clinically normal (20 eyes) and myopic subjects (101 eyes).
Results: The dataset consists of clear (121) and manually marked (121) images resulting in a total of 242 images. The age distribution for all subjects combined is 27.24 ± 9.28 (range, 11.0– 69.0) years. For normal subjects mean ± SD age distribution is 32.40 ± 17.23 years. Similarly, the myopia age distribution is 26.22 ± 6.37 years. Ground truth images, ie, manually segmented by a clinical expert are provided along with other meta-data includes age, gender, laterality, refractive error classification, spherical equivalent (SE), best-corrected visual acuity (BCVA), intraocular pressure (IOP), and axial length (AXL).
Conclusion: This open, public database is online at the ICPSR website of the University of Michigan. The dataset can be used to test and validate newly developed automated segmentation algorithms.

Keywords: optic disc tilt, high myopia, image segmentation, image database, ophthalmology, optical coherence tomography

Introduction

Optic disc tilt (ODT) or Tilted optic disc is a common finding in the general population and is due to anomalous human development1,2 caused by the malclosure of the embryonic optic fissure.2,3 High myopia,1–4 astigmatism,2 visual field loss,1,5 defective color vision,1 and retinal abnormalities are commonly associated with ODT. Usually ODT is considered to be non-progressive1 except in cases of progressive myopia. The anomalous ODT can be misdiagnosed, as for example, in glaucoma.1,5 The prevalence of ODT is reported to be the highest (37.0%) amongst myopic Asian subjects.6

In myopic eyes with increasing axial length, the optic nerve head loses its original anatomical size and shape.7,8 In addition to change of the optic disc to a vertical oval shape, a parapapillary gamma zone develops and enlarges at the temporal disc border.7,8 In eyes with long axial lengths, (>26.0 mm) the Bruch’s membrane opening (BMO) diameter increases both horizontally and vertically.9 Likewise, the Gamma zone may develop due to an axial elongation associated with BMO enlargement.9 The characteristics of the peripapillary retinal nerve fiber layer (RNFL) thickness is also associated with the degree of temporal myopic ODT.10,11 Hence, while interpreting the RNFL thickness in myopic eyes the degree of myopic ODT should be considered.10,11

The common methods to image the optic disc is by fundus photography, optical coherence tomography (OCT) and confocal scanning laser ophthalmoscopy (Table 1). OCT helps the clinician to image the layers of the retina non-invasively and provides high-speed 3D images of high-quality retinal, optic nerve head, and choroidal vasculature images. OCT images is a useful tool to differentiate true condition/diseases from pseudo status. For example, OCT optic disc images can help in differentiate between true optic disc edema and pseudoedema.12 Table 1 summarizes the various techniques that have been presented in the literature.

Table 1 A Summary of Optic Disc Tilt Assessment Methods

Currently, to the best of our knowledge no imaging instrument has an inbuilt method/algorithm to quantify the ODT. There are many ways to segment the ODT and hence quantify the angle of tilt. Clinicians can use manual or system generated marking. Manual marking is desired but is dependent upon the availability of a trained clinician. In general, during clinical examination the ODT is not quantified due to non-availability of easy methods or tools. Recently, authors presented an automated segmentation ODT algorithm for use with OCT images.13 The results from this methodology were compared with ground-truth (manually marked by an expert clinician) and the accuracy was reported to be 80.00%.

The availability of real world datasets is essential in accelerating health data science data analytics, including the use of routinely collected data to drive new discoveries and innovations.45 Khan et al,45 reported out of 140 unique datasets, 94 raw datasets alone were available for open access. The current paper describes here a dataset for optic nerve head OCT images from myopic subjects. This is to the best of knowledge only dataset dealing with this. This dataset aims to make open access raw ophthalmic ODT OCT images for further analysis and to test out new image processing segmentation algorithms.

Construction and Content

Image Resources

Data from Subjects who visited a tertiary care ophthalmic center in Chennai, India between January 2019 to December 2020 for ophthalmic consultation and underwent OCT imaging are included. All individuals who came for comprehensive ophthalmic examination had signed the written informed general consent agreement prior to their eye examination and approved the use of their data for research purposes. The current study was approved by the IRB of the Vision Research Foundation, Chennai, India and was conducted in accordance with the tenets of the declaration of Helsinki.

The optic nerve head was imaged using a commercially available Spectral-Domain OCT (Cirrus 5000, Carl Zeiss Meditec Inc., Dublin, CA). The optic disc cube 200×200 program was used and all the images were aligned with the center of the optic nerve head. All OCT images were 8-bit grayscale images of dimensions of 200×200 pixels corresponding to 6 mm x 6 mm (894 x 596 pixels). Images with a signal strength of 7 or higher than was included.

Demographic and Clinical Parameters

This dataset consists of a set of optic disc images (vertical and horizontal cross-sectional) from 67 subjects (34 Female, 33 Male) imaged by OCT. These datasets cover both clinical normal and also images of myopic subjects. Table 2, gives details on the dataset which includes 20 healthy normal and 101 myopic OCT images (total 121 images, 60 males, 61 females). These images are divided into three groups: 20 emmetropes (EMM) (SE 0.00 to > −0.50 D), 70 low-moderate myopes (LMM) (SE <-0.50 to −6.00 D), 31 high myopes (HM) (SE <-6.12 D).46

Table 2 Image Details of ODTiD Dataset

In addition to unsegmented optic disc OCT images, the dataset also contains corresponding ground-truth images (each image was manually segmented by an experienced clinician), as well as meta-data, namely age in years, gender, their refractive error as spherical equivalent, refractive classification, BCVA, IOP measured (in mmHg) with Goldmann applanation tonometer, and axial length (in mm) data measured using the non-contact and high-resolution optical biometric device IOLMaster 700 (Carl Zeiss Meditec AG, Jena, Germany).

Characteristics of the Dataset

The Dataset consists of clear (121) and manually marked (121) images resulting in a total of 242 images. These 121 images include patients with myopia (101) and clinically normal (20) images. The age distribution for all subjects combined is 27.24 ± 9.28 (range, 11.0–69.0) years. For normal subjects mean ± SD age distribution is 32.40 ± 17.23 years. Similarly, the myopia age distribution is 26.22 ± 6.37 years.

Manual Marking of Images

Manual marking of all images was done by a trained single clinical expert (JJB). The Cirrus 5000 (Carl Zeiss Meditec Inc., Dublin, CA) provides a cross-sectional image for both horizontal and vertical. The clinician manually drew two straight line aligning the upper boundary RPE layers using a mouse and MS Paint. The boundary line with red-green color used is shown in Figure 1. A caveat should be inserted here - since these boundaries were marked using a mouse it is prone to error because of excessive sliding of the mouse and/or parallax.

Figure 1 ODT images (A) non marked horizontal scanned image, (B) manually marked horizontal scanned image (ground truth), (C) non marked vertical scanned image, and (D) manually marked vertical scanned image.

Segmentation of RPE Boundary

New segmentation algorithms can be developed using the clear images. Manually marked images can then be used to compare the segmentation achieved with new algorithms. For comparisons the boundary method suggested by Gudapati et al,13 or other methods can be used. Gudapati et al,13 for example, introduced various methods and comparisons were then made for each parameter. The source code for the image processing algorithm can be found at https://github.com/gnitish18/OpticDisc-TiltAngle.13

Utility and Discussion

Differentiating physiological ODT from a disease involved ODT is clinically important.12 Recent reports have suggested that optic disc imaging with OCT can improve differential diagnosis involving optic nerve head diseases. Creating and making accessible large and real-world datasets has been essential in accelerating public health database research.45 To the best of our knowledge this the first publicly available dataset on optic nerve head cross-sectional imaged with OCT. Detailed calculations of ODT parameters from the ODT dataset has been completed and their results have been published elsewhere.13 These calculations can be used as a reference for future algorithms. The ODTiD database can be divided into training and test sets for application in machine learning/deep learning methods. This database is available for use by researchers and can be downloaded from the ICPSR website at the University of Michigan (https://doi.org/10.3886/E137701V3).47 In the future additional marked and non-marked images will be included with their detailed characteristics.

Conclusions

This publicly available, open-access OCT images collection will serve as a dataset for use in biomedical image processing. This dataset will be optimal for researchers aiming to develop quantitative relationships between ODT and pathological conditions such as myopia.

Abbreviations

ODT, The optic disc tilt; SD-OCT, Spectral-domain optical coherence tomography; SE, Spherical equivalent; BCVA, Best corrected visual acuity; IOP, Intraocular pressure; AXL, Axial length; BMO, Bruch’s membrane opening; RNFL, retinal nerve fiber layer; OCT, Optical coherence tomography; BW, Black and white; CFP, Color fundus photography; FFA, Fundus fluorescein angiography; FP, Fundus photography; NA, Not available; OD, Optic disc; SLP, Scanning laser polarimetry; EMM, Emmetropes; LMM, Low-moderate myopes; HM, High myopes.

Data Sharing Statement

All the clinical data and materials supporting the manuscript are maintained in our Hospital. This image dataset and clinical parameters are available for use by researchers on the ICPSR website at the University of Michigan (https://doi.org/10.3886/E137701V3).

Ethics Approval and Consent to Participate

This study was approved by the Institutional Review Board of the Vision Research Foundation, Chennai, India. The study conformed to the tenets of the Declaration of Helsinki, and signed informed consent was obtained from all subjects.

Acknowledgments

This work was partly supported by a DISCOVERY Grant from the Natural Sciences and Engineering Research Council of Canada to V. L. The authors thank Mr. V.K. Viekash, for his support in formatting the OCT images.

Funding

There is no funding to report.

Disclosure

The authors declare that they have no competing interests.

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