# OCTDL: Optical Coherence Tomography Dataset for Image-Based Deep Learning
Methods

Mikhail Kulyabin  Pattern Recognition Lab, Department of Computer Science,
Friedrich-Alexander-Universität Erlangen-Nürnberg, Martensstr. 3, 91058
Erlangen, Germany  corresponding author: Mikhail Kulyabin
<EMAIL_ADDRESS> Aleksei Zhdanov  Engineering School of Information
Technologies, Telecommunications and Control Systems, Ural Federal University
Named after the First President of Russia B. N. Yeltsin, Yekaterinburg, Mira,
32, 620078, Russia  Anastasia Nikiforova  Ophthalmosurgery Clinic
"Professorskaya Plus", Yekatrinburg, Vostochnaya, 30, 620075, Russia  Ural
State Medical University, Yekaterinburg, Repina, 3, 620028, Russia  Andrey
Stepichev  Ophthalmosurgery Clinic "Professorskaya Plus", Yekatrinburg,
Vostochnaya, 30, 620075, Russia  Anna Kuznetsova  Ophthalmosurgery Clinic
"Professorskaya Plus", Yekatrinburg, Vostochnaya, 30, 620075, Russia  Mikhail
Ronkin  Engineering School of Information Technologies, Telecommunications and
Control Systems, Ural Federal University Named after the First President of
Russia B. N. Yeltsin, Yekaterinburg, Mira, 32, 620078, Russia  Vasilii Borisov
Engineering School of Information Technologies, Telecommunications and Control
Systems, Ural Federal University Named after the First President of Russia B.
N. Yeltsin, Yekaterinburg, Mira, 32, 620078, Russia  Alexander Bogachev
Ophthalmosurgery Clinic "Professorskaya Plus", Yekatrinburg, Vostochnaya, 30,
620075, Russia  Ural State Medical University, Yekaterinburg, Repina, 3,
620028, Russia  Sergey Korotkich  Ophthalmosurgery Clinic "Professorskaya
Plus", Yekatrinburg, Vostochnaya, 30, 620075, Russia  Ural State Medical
University, Yekaterinburg, Repina, 3, 620028, Russia  Paul A Constable
Flinders University, College of Nursing and Health Sciences, Caring Futures
Institute, Adelaide, Australia  Andreas Maier  Pattern Recognition Lab,
Department of Computer Science, Friedrich-Alexander-Universität Erlangen-
Nürnberg, Martensstr. 3, 91058 Erlangen, Germany

###### Abstract

Optical coherence tomography (OCT) is a non-invasive imaging technique with
extensive clinical applications in ophthalmology. OCT enables the
visualization of the retinal layers, playing a vital role in the early
detection and monitoring of retinal diseases. OCT uses the principle of light
wave interference to create detailed images of the retinal microstructures,
making it a valuable tool for diagnosing ocular conditions. This work presents
an open-access OCT dataset (OCTDL) comprising over 1600 high-resolution OCT
images labeled according to disease group and retinal pathology. The dataset
consists of OCT records of patients with Age-related Macular Degeneration
(AMD), Diabetic Macular Edema (DME), Epiretinal Membrane (ERM), Retinal Artery
Occlusion (RAO), Retinal Vein Occlusion (RVO), and Vitreomacular Interface
Disease (VID). The images were acquired with an Optovue Avanti RTVue XR using
raster scanning protocols with dynamic scan length and image resolution. Each
retinal b-scan was acquired by centering on the fovea and interpreted and
cataloged by an experienced retinal specialist. In this work, we applied Deep
Learning classification techniques to this new open-access dataset.

## Background & Summary

Optical coherence tomography (OCT) is a non-invasive imaging modality that is
of great importance in clinical ophthalmology [1, 2]. OCT is one of the most
widely used, rapidly developing medical imaging technologies. Today,
visualization of the neural tissue is not limited to the macular area as it
was at the beginning of OCT [3] but also to the vascular structures as well
[4]. OCT imaging of the retina was first proposed by Huang et al. [5] in 1991.
OCT utilizes the basic principle of low coherent light interferometry to
detect the backscattered near-infrared light to reconstruct the depth profile
of the biological tissue sample. The relatively low resolution of the first
OCT devices has been gradually improved so that the image quality is now able
to resolve more subtle changes in retinal morphology. Numerous studies have
shown that OCT can be used in monitoring and confirming many common and sight-
threatening ocular conditions, such as glaucoma [6], diabetic retinopathy [7],
and age-related macular degeneration [8].

In this work, we present a new open-access OCT dataset for Image-Based Deep
Learning Methods (OCTDL) comprising over 1600 high-resolution OCT images
labeled according to various pathological conditions. The OCTDL dataset
includes macular raster scans of Age-related Macular Degeneration (AMD),
Diabetic Macular Edema (DME), Epiretinal Membrane (ERM), Retinal Artery
Occlusion (RAO), Retinal Vein Occlusion (RVO), and Vitreomacular Interface
Disease (VID) with the following pathological conditions: Macular Neovascular
membranes (MNV), Disorganization of Retinal Inner Layers (DRIL), drusen,
Macular Edema (ME), and Macular Hole (MH). We also analyzed OCT scans from
existing public datasets and applied Deep Learning (DL) classification methods
to these as well as to the OCTDL dataset and with combinations of the OCTDL
dataset and publicly available datasets. Table 1 lists a comparative analysis
of published OCT datasets: Kermany [9] dataset, published in 2019, remains the
most extensive in terms of the number of OCT images. The second largest OCT
image open-access dataset is provided in our new dataset, OCTDL, which is
described in this work. The most represented diseases in the published
datasets are AMD (more than ten times), DME (more than three times), and
central serous chorioretinopathy (CSC) (more than three times). The most
common equipment used for capturing OCT images was the Heidelberg Engineering
Spectralis and Zeiss Cirrus systems, as these OCT systems provide high-
resolution and wide-spectrum eye images for diagnosing various ocular
conditions.

### Open-access datasets

The RETOUCH [10] dataset was sourced from the retinal OCT fluid challenge of
MICCAI 2017. This dataset features 70 OCT volumes labeled for retinal fluid
types — intra-retinal fluid (IRF), sub-retinal fluid (SRF), and pigment
epithelial detachment (PED), related to ME secondary to AMD and RVO. The
training data incorporated varying volumes from different OCT systems (Cirrus,
Triton, Spectralis) labeled for different types of fluid manually by
experienced human graders. The B-scans were annotated at the Medical
University of Vienna and Radboud University Medical Center. The RETOUCH
dataset is widely utilized in multiple studies related to retinal fluid
classification and segmentation [11].

The University of Minnesota (UMN) [12] dataset comprises 600 OCT B-scan images
from exudative AMD subjects. Each subject’s data includes approximately 100
B-scans, with the most significant area containing fluid chosen for exporting.
The dataset includes manual annotation of IRF, SRF, and PED regions, enabling
validation of segmentation algorithms. Challenges include a large number of
fluid regions, making segmentation a complex task.

The OPTIMA [13] dataset, derived from the MICCAI 2015 cyst segmentation
challenge, provides 30 macular volumes collected from different ophthalmic OCT
devices: Cirrus, Spectralis, Topcon, and Nidek. This dataset is primarily used
for IRF segmentation and was annotated by experienced human graders. The
dataset was split into training and testing subsets with the macular scans.
The challenge with this dataset is the precise localization of IRF
segmentation areas contained in the volumes obtained from different devices.

The Duke [14] dataset is a public dataset provided by Duke University,
featuring 110 annotated OCT B-scans from patients with severe DME. The scans
are annotated with eight retinal layer boundaries, aiding the training and
testing of segmentation algorithms. Special attention was given to anonymity,
enabling public access to the dataset.

The healthy controls multiple sclerosis (HCMS) [15] dataset, provided by the
Johns Hopkins University, contains OCT scans of 35 subjects featuring both
healthy and multiple sclerosis subjects. The scans are annotated to limited
semantic fluid regions, with additional preprocessing required to validate
segmentation performance.

The Kermany [9] dataset, with 207130 OCT B-scan images, was constructed to
categorize conditions including choroidal neovascularization (CNV), DME,
drusen, and normal. Annotations were done by tiered graders, enabling an
extensive dataset for retinal fluid labels in maculopathies.

The open-access OCTID [16] dataset comprises more than 500 high-resolution OCT
images categorized across distinct pathological conditions. The dataset
encompasses normal, MH, AMD, Central Serous Retinopathy (CSR), and Diabetic
Retinopathy (DR). The dataset images are from raster scans, with a 2mm scan
length and a resolution of 512x1024 pixels. Moreover, 25 normal OCT images are
supplemented with precise delineations for accurate OCT image segmentation
evaluation. The dataset serves as a valuable resource for early diagnosis and
monitoring of retinal diseases.

The OCTDL dataset, reported here, comprises 1618 images categorized into
various diseases and eye conditions. These high-resolution OCT B-scans allow
the visualization of the retinal layers centered on the fovea, the posterior
vitreous body, and the choroidal vessels. This large open-access dataset is
provided to aid in the diagnosing and monitoring of retinal diseases. The
dataset was released for research and algorithm development, and it offers
fully labeled images to advance automatic processing and early disease
detection. Updates are planned for ongoing enhancement with additional
clinical populations and samples.

Table 1: Comparative analysis of published OCT datasets. Year | Dataset | Dataset Size | Equipment Used | Labels | Access
---|---|---|---|---|---
2015 | Duke [14] | 110 B-scan images | Not specified | Severe DME | open
2016 | OPTIMA [13] | 30 volumes | Cirrus, Topcon | IRF | open
2017 | Lee [17] | 1289 B-scan images | Spectralis | ME | limited
2017 | UMN [12] | 600 B-scan images | Spectralis | Exudative AMD | open
2018 | Kermany [9] | 207130 B-scan images | Spectralis | CNV, DME, Drusen, NO | open
2018 | Schlegl [18] | 1200 B-scan volumes | Cirrus, Topcon | AMD, DME, RVO | limited
2018 | OCTID [16] | 500 B-scan images | Not specified | MH, AMD, CSR, DR, NO | open
2018 | Venhuizen [19] | 221 volumes | Spectralis | AMD | limited
2019 | Hu [20] | 100 volumes | Not specified | SRF, PED | limited
2019 | RETOUCH [10] | 70 volumes | Cirrus, Triton | AMD, RVO | open
2019 | HCMS [15] | 35 volumes | Spectralis | Healthy Controls, MS | open
2019 | Gao [21] | 52 volumes | Spectralis | CSC | limited
2019 | Rao [22] | 150 volumes | Cirrus | Sub-retinal fluid segmentation | limited
2020 | Yang [23] | 103 volumes | Cirrus | CSC | limited
2020 | Bao [24] | 240 B-scan images | Not specified | AMD, PED | limited
2021 | Pawan [25] | 25 volumes | Cirrus | CSC | limited
2023 | OCTDL | 1618 B-scan images | Optovue Avanti | AMD, DME, ERM, NO, RAO, RVO, VID | open

### Limited access datasets

Schlegl et al. [18] dataset contains 1200 OCT B-scan volumes associated with
AMD, DME, and Retinal Vein Occlusions, segmented by two experienced retinal
specialists, to enable quantification of macular fluid in these conditions.

Gao et al. [21] provides 52 B-scan volumes that of Central Serous
Chorioretinopathy (CSC). Their work introduced a deep learning model, double-
branched and area-constraint fully convolutional networks (DA-FCN), which
provides substantial high performance in segmenting subretinal fluid.

Lee et al. [17] dataset features 1289 B-scan images, which were provided to
aid in the automated segmentation of ME using a convolutional neural network
(CNN) to demonstrate high concordance between machine learning and expert
human segmentation of the OCT scans.

Rao et al. [22] OCT dataset consists of 150 macular volumes for retinal fluid
segmentation that were used to study the effects of signal noise and motion
artifacts in segmenting sub-retinal fluid.

Yang et al. [23] dataset has 103 OCT volumes that were used for the automatic
assessment of neurosensory retinal detachment and introduced the residual
multiple pyramid pooling network (RMPPNet) to address segmentation challenges
in Spectral Domain OCT images.

Bao et al. [24] dataset comprised 240 B-scans for PED segmentation. The
attention multi-scale network (AM-Net) architecture was used to address the
uneven sizes of PED and achieved accurate segmentation in the OCT-B scans.

Pawan et al. [25] dataset of 25 macular volumes aimed at segmenting SRF from
central serous chorioretinopathy (CSCR) OCT images, and employed an enhanced
SegCaps architecture, termed DRIP-Caps that provided an advanced alternative
to existing models in segmentation of fluid in CSCR.

Hu et al. [20] dataset comprised 70 training, 15 testing, and 15 cases
containing 126 scans each to segment SRF and PED lesions, using deep neural
networks together with atrous spatial pyramid pooling (ASPP).

Venhuizen et al. [19] collected 221 OCT volumes (6158 B-scans) to segment
intraretinal cystoid fluid (IRC) using a neural network cascade that
significantly boosted performance by incorporating prior anatomical
information.

## Methods

The B-scan OCT images were acquired using a raster scanning protocol with
dynamic scan length and image resolution and obtained with an Optovue Avanti
RTVue XR. Each retinal scan was taken after centering the scan area over the
macular fossa (fovea) and further interpreted and cataloged by an experienced
retinal specialist. Axial and transverse resolutions were 5 $\mu$m and 15
$\mu$m, respectively. A superluminescent diode (SLD) with a wavelength of 840
nm served as the optical source. A beam of light directed toward the tissues
forms an interference pattern with back-reflected light from the retina. This
occurs due to the interaction of waves reflected from the tissue surface and
waves that have traveled deep into the tissue. The back-reflected waves travel
back to the beam splitter, where interference occurs. The interference fringes
are detected by a detector that records the phase difference between the back-
reflected waves. By measuring the difference in the time delay of interference
fringes as a function of depth in the tissue, a 2D image of the internal
structures of the retina is created. This method produces detailed, high-
resolution images of the eye’s internal structures. Each image pixel’s light
intensity corresponds to the wave reflected from a certain depth. Black-and-
white images are formed based on different intensities of reflected light from
various retina structures supra- and underlying tissues. Fig.1 shows an OCT
image of a healthy normal retina of the fovea with retinal and choroidal
structures. In Fig.1, dark areas (hyporeflective: 2, 8, 9, 16) may correspond
to places where light is absorbed or scattered, and light (hyperreflective: 1,
3, 13, 14, 15) areas to places where back reflection occurs. Thus, black-and-
white images visualize tissue structures and layers based on their optical
properties and reflect differences in the intensity of light received from
different depths.

The resulting images were marked by a practicing medical retina
ophthalmologist. Labeling of the scans was made based on the retinal pathology
and the patient’s clinical diagnosis.

The study was approved by the ethics committee of Ural Federal University
Named after the First President of Russia B. N. Yeltsin (Conclusion No. 1,
dated 1 February 2023). Informed consent was obtained from all subjects
involved in the study.

Figure 1: Structure of the posterior segment of the eye as visualized with OCT
B-scan and labelled accordingly from inner to outer retina. 1 - Posterior
Hyaloid Membrane; 2 - preretinal space; 3 - retinal nerve fiber layer and
inner limiting membrane; 4 - ganglion cell layer; 5 - inner plexiform layer; 6
- inner nuclear layer; 7 - outer plexiform layer; 8 - outer nuclear layer; 9 -
Henle’s nerve fiber layer; 10 - external limiting membrane; 11 - myoid zone of
the photoreceptors; 12 - ellipsoid zone of the photoreceptors; 13 - outer
segments of the photoreceptors; 14 - interdigitation zone of the
photoreceptors; 15 - retinal pigment epithelium and Bruch’s membrane; 16 -
choriocapillarises.

## Data records

The OCTDL dataset contains more than 1600 high-resolution images. All images
are stored in JPG format in separate folders corresponding to the label. The
name of each image consists of a shortcut of the corresponding disease and a
sequence number. A separate CSV file stores the labels with classification by
pathological conditions for all OCT scans. Table 2 shows the distribution of
images in the dataset. Data was collected from more than 1000 patients aged 50
to 85 years, with a male-to-female ratio of 3:2 and mean age of 63 in
Yekaterinburg, Russia. In this section, we provide a brief description of each
of the disease groups.

Table 2: Dataset distribution by a corresponding disease. Disease | Label | Number of Image Scans
---|---|---
Age-related Macular Degeneration | AMD | 885
Diabetic Macular Edema | DME | 143
Epiretinal Membrane | ERM | 133
Normal | NO | 284
Retinal Artery Occlusion | RAO | 22
Retinal Vein Occlusion | RVO | 93
Vitreomacular Interface Disease | VID | 58
Total |  | 1618

#### Age-related Macular Degeneration

AMD is an acquired retinal degeneration that causes significant central visual
impairment resulting from a combination of non-neovascular (drusiform and
abnormalities of the retinal pigment epithelium) and neovascular abnormalities
(neovascular choroidal membrane formation). Disease progression may include
focal areas of retinal pigment epithelium (RPE) loss, subretinal (sub-RPE)
hemorrhages or serous fluid, and subretinal fibrosis [26]. Clinically, these
late changes manifest with loss of central vision, ranging from low vision to
blindness [27].

AMD is defined by specific changes in the macular, particularly the deposition
of focal yellow extracellular deposits known as drusen 2a. On OCT, drusen
appear as rounded mounds in the space between Bruch’s membrane and the
basolateral membrane of the RPE and have a homogeneous reflectivity. Drusen
are indicators of RPE stress and may be monitored for changes periodically by
a medical retinal specialist [28].

((a)) Initial stage

((b)) Intermediate stage

((c)) Intermediate stage

Figure 2: Age-related Macular Degeneration (AMD). Initial stage (a) with an
arrow indicating a solitary hard drusen deposit on Bruch’s membrane below the
basolateral membrane of the retinal pigment epithelium; Intermediate stage (b)
with medium-sized cuticular drusen which gives a ribbon-like or saw-tooth
pattern of hyperreflectivity on OCT indicated by the arrow; Intermediate stage
(c) with drusenoid detachment of retinal pigment epithelium with
hyporeflective subretinal space filled with fluid and the retinal pigment
epithelium detached from Bruch’s membrane.

As the disease progresses, the number of drusen becomes more extensive, and
they tend to fuse and enlarge, becoming confluent. Cuticular drusen [29] are
drusen that cluster at the macular region and have a characteristic saw tooth
and double layer appearance on OCT (Fig.2b). One possible complication is
drusenoid retinal pigment epithelium [30] (Fig.2c). Both conditions do not
indicate starting treatment but require more frequent reviews and may require
additional diagnostic methods to exclude the presence of any
neovascularization in the choroid or sub-retinal space such as angiography.

Fig.3 shows examples of AMD: the retinal profile is deformed, and the normal
foveal architecture is disrupted. In Fig.3a the inner retinal layers are
thinned and contain outer retinal tubulations or cystic spaces, highlighted
with number 1. Subfoveolarly, a hyporeflective region is visible beneath the
retinal pigment epithelium - in Fig.3a highlighted with number 2.
Hyperreflective coloration of the choriocapilaris below the RPE layer atrophy
is apparent. Local and diffuse decreases in the thickness of the
choriocapillaris layer. Fig.3b shows different fluid-filled spaces in the
macular that may accompany the clinical features of AMD:

  * •

Subretinal fluid - space between the RPE and the neurosensory retina, in
Fig.2b is shown with number 1.

  * •

Intraretinal fluid, a kind of hyperreflective cyst - a cyst in the inner
retina, but the content differs in reflectivity - with a granular appearance
indicating the presence of more reflective elements that may be cellular
debris or protein that has leaked into the space, in Fig.2b is shown with
number 2.

  * •

Sub-retinal pigment epithelial fluid - a hyporeflective space between Bruch’s
membrane and the basolateral membrane of the RPE in Fig.2b is shown with
number 3. This may be due to the breakdown of fluid regulation by the ion
channels of the RPE [31].

((a)) Markers

((b)) Types of fluid

Figure 3: Age-related Macular Degeneration (AMD). Markers (a): 1 - outer
retinal tubulation or cystic spaces; 2 - Subretinal fibrosis causing
distortion of the macular and hyporeflectivity of the underlying choroid.
Types of fluid (b): 1 - subretinal fluid; 2 - intraretinal fluid; 3 - sub-
retinal pigment epithelial fluid accumulation.

#### Diabetic Macular Edema

DME is the most common cause of vision loss in patients with diabetic
retinopathy, with an increasing prevalence associated with the global epidemic
of type 2 diabetes mellitus [32, 33].

  * •

Hard exudates (HE) are defined as deposits of hyperreflective material
replacing retinal tissue without increasing the underlying retinal thickness,
and are considered an unfavorable sign representing the break down of the
inner blood-retinal barrier with the potential to reduce visual acuity - in
Fig.4a is shown with number 1.

  * •

Intraretinal fluid (IRF) appears as heterogeneous sized cavities with
hyporeflective content due to their fluid content; slight retinal thickening
may indicate initial changes of fluid accumulation with focal retinal edema
that may precede the appearance of multiple cystic spaces - in Fig.4a is shown
with number 2.

((a)) DME

((b)) DRIL

Figure 4: (a) Signs of Diabetic Macular Edema (DME): 1 - Hard exudates (HE), 2
- Intraretinal fluid (IRF), 3 - Hyperreflective foci; (b) Disorganization of
retinal inner layers (DRIL).

DRIL is an OCT biomarker, indicating poor definition of the boundaries of the
inner retinal layers - in Fig.4b, DRIL is indicated by number 1. DRIL occurs
in patients with various retinal vascular diseases with prolonged presence of
intraretinal fluid, such as DME, or following a vascular occlusion, such as
RVO. The degree of DRIL indicates the severity of the disease and correlates
with the patient’s visual acuity prognosis. DRIL may persist even after the
resolution of edema following treatment or in advanced stages of the disease
[34].

#### Retinal Vein Occlusion

Secondary ME is the leading cause of visual loss in patients with central
retinal vein occlusion (CRVO). OCT is the critical imaging modality to
diagnose and formulate a treatment plan for cystic macular edema (CME) of this
etiology. In contrast to DME, the ME secondary to a branch or CRVO is
generally cystic and localized to the inner retina following leakage from
engorged veins, Fig.5a. OCT scans also show a higher level of
hyperreflectivity of the inner retina due to ischemia. The long-term prognosis
of vein occlusion will depend on the degree of ischaemic damage to the retinal
tissue and the structural damage to the neural pathways after CME resorption.
The presence and severity of any DRIL is an indicator of likely visual
prognosis [35, 36].

((a)) Retinal Vein Occlusion

((b)) Retinal Artery Occlusion

Figure 5: Retinal Vein Occlusion (RVO). Cystic macular edema in central
retinal vein thrombosis. (a): 1 - Intraretinal fluid (IRF), 2 -
hyperreflectivity of the inner retinal layers; Signs of Retinal Artery
Occlusion (RAO) (a): 1 - Increased hyperreflectivity of the inner retina
following ischemia, 2 - prominent middle limiting membrane (p-MLM).

#### Retinal Artery Occlusion

Occlusion of the central retinal artery (CRAO) and its branches (BRAO) leads
to the formation of acute tissue ischemia, giving a specific OCT picture -
pronounced hyperreflectivity, loss of homogeneity, and edema of the inner
parts of the retina containing the ganglion cells, Fig.5b. Also, a biomarker
of acute ischemia is a prominent middle limiting membrane (p-MLM) - a
hyperreflective line or band located in the inner part of the outer plexiform
layer at the border with the outer nuclear layer. It is not ordinarily
visible, which appears in the early period of the pathological damage and is
due to opacification of the middle retinal layers [37].

#### Vitreomacular Interface Disease

VID is a term used to describe a group of diseases resulting from the
pathologic course of the normal age-associated process of a posterior vitreous
detachment. Usually, the process is completed without retinal deformation.
However, vitreo-retinal traction occurs in cases of adhesion between the
retina and vitreous body, which can lead to macular tears, cysts, or holes
developing [38].

  * •

When pathologic adhesion of the posterior hyaloid to the retinal interface
forms, progressive posterior vitreous detachment causes axial traction of the
inner limiting membrane, formed by Müller cell end feet that deforms the
retinal tissue, Fig.6a.

  * •

Macular retinal hole is a complete defect in the inner layers of the retina
that extends to the RPE, Fig.6b. IRF appears as different-sized cavities with
hyporeflective contents. In macular retinal tears, the intraretinal fluid is
contained within the borders of the tear [39].

  * •

One of the variants of MH with preservation of the integrity of the
photoreceptor layer is a lamellar tear of the neurosensory retina, Fig.6c. The
condition is often asymptomatic and requires no treatment, but regular
monitoring by a medical retina specialist is advised.

((a)) Vitreomacular traction syndrome

((b)) Retinal interface disorder, complete macular tear

((c)) Lamellar tear

Figure 6: Vitreomacular Interface Disease (VID). Vitreomacular traction
syndrome (a): 1 - Posterior hyaloid membrane, 2 - Vitreomacular adhesion zone,
3 - Emerging neurosensory retinal defect; Retinal interface disorder (b): 1 -
intraretinal fluid (IRF), 2 - Edges of the tear, 3 - detached posterior
hyaloid membrane; Lamellar tear (c).

### Epiretinal Membrane

ERM can develop idiopathically, secondary to intraocular surgery or
inflammation, and are characterized by the proliferation of glial tissue on
the retina’s inner surface in the macular area, Fig.7a. The Pathologic
connective tissue overgrowth results in epiretinal fibrosis (fibrosis of the
inner border membrane, epiretinal membrane). Clinically, the disease is
characterized by thickening and wrinkling of the inner limiting membrane,
sometimes called cellophane retinopathy, because of its appearance on fundus
examination[40].

((a))

((b))

Figure 7: VID by the epiretinal membrane (a); ERM with foveola deformity and
Ectopia (b): 1 - ERM, 2 - Ectopia.

In epiretinal membrane maturation, the vireo-retinal traction can deform the
retina, reducing visual acuity, cause metamorphopsia, and can lead to macular
tears and holes. In such cases, there is an irreversible loss of visual
function without timely surgical intervention requiring an epiretinal membrane
peel[41].

## Technical Validation

In this work, we tested the performance of the DL architectures VGG16 [42] and
ResNet50 [43] on our dataset (OCTDL). VGG16 and ResNet50 are well-established
and widely recognized convolutional neural networks (CNN). They have been
extensively studied and benchmarked on various OCT datasets [44, 45, 46].
Therefore, We can establish a strong baseline for the OCTDL dataset’s
performance using these architectures. VGG and ResNet are considered classical
architectures. However, they still perform remarkably well on many image
classification problems [47, 48, 49].

VGG16 is a 16-layer, relatively extensive DL network with 138 million
parameters. However, the simplicity of the VGG16 architecture is its main
attraction. VGG16 has 13 convolutional layers and three fully connected
layers, each followed by a ReLU activation function, five max pooling
operations, and a softmax activation function.

ResNet was based on the VGG neural networks. However, a ResNet has fewer
filters and is less complex than a VGG. Using shortcut connections, ResNet
provided a novel way to use more convolutional layers without running into the
vanishing gradient problem [50]. A shortcut connection skips some layers,
converting a regular network to a residual network. The ResNet50 is a 50-layer
CNN that consists of 48 convolutional layers, one MaxPool layer, and one
average pool layer.

The OCTDL dataset was randomly split into training, validation, and test
subsets in the proportion of 70:10:20, with all classes performed in all
subsets. For all experiments, we used the Cross-Entropy loss function and
Adaptive Moment Estimation (ADAM) optimizer with a 0.0005 learning rate. For
data augmentation, we used random crop, horizontal and vertical flips,
rotation, translation, and Gaussian blur.

The OCTDL dataset contains a file with labels for each image, which can be
used to navigate from the disease to the corresponding pathological
condition(s). This is necessary, for example, to combine different available
datasets. Thus, for experiments, we combined OCTDL with OCTID and Kermany
datasets. DME is a particular case of DR, and MH is a particular case of VID,
so we can combine them into one category for classification purposes. Drusen
and MNV are the early and late stages of AMD, respectfully. OCTDL and OCTID
datasets were mixed and randomly split into subsets. For Kermany, we used
OCTDL as a test subset.

The following presents the results of training neural networks exclusively on
our dataset and combining our dataset with the OCTID and Kermany datasets to
solve the classification problem. Confusion matrices for training on ResNet50
and VGG16 with our proposed dataset are presented in Fig.8. As metrics, we
used Accuracy (ACC), F1 score, Area Under the Curve (AUC), Precision (P), and
Recall (R). Table 3 summarizes the results of the experiments.

((a)) ResNet50

((b)) VGG16

Figure 8: Confusion matricies of ResNet50 (a) and VGG16 (b) models, trained on OCTDL dataset. Table 3: Resulting metrics on different combinations of datasets. Model | Dataset | Labels | ACC | F1 | AUC | P | R
---|---|---|---|---|---|---|---
ResNet50 | OCTDL | AMD, DME, ERM, NO, RAO, RVO, VID | 0.891 | 0.891 | 0.985 | 0.895 | 0.891
VGG16 | OCTDL | AMD, DME, ERM, NO, RAO, RVO, VID | 0.895 | 0.895 | 0.980 | 0.896 | 0.895
ResNet50 | OCTID | AMD, CSR, DR, MH, NO | 0.923 | 0.927 | 0.979 | 0.932 | 0.923
VGG16 | OCTID | AMD, CSR, DR, MH, NO | 0.932 | 0.933 | 0.970 | 0.939 | 0.932
ResNet50 | Kermany | CNV, DME, Drusen, NO | 0.998 | 0.998 | 0.999 | 0.998 | 0.998
VGG16 | Kermany | CNV, DME, Drusen, NO | 0.998 | 0.998 | 0.999 | 0.998 | 0.998
ResNet50 | OCTID + OCTDL | AMD, DR, MH, NO | 0.957 | 0.955 | 0.996 | 0.954 | 0.957
VGG16 | OCTID + OCTDL | AMD, DR, MH, NO | 0.975 | 0.977 | 0.998 | 0.979 | 0.975
ResNet50 | Kermany + OCTDL | CNV, DME, Drusen, NO | 0.833 | 0.805 | 0.963 | 0.823 | 0.833
VGG16 | Kermany + OCTDL | CNV, DME, Drusen, NO | 0.818 | 0.798 | 0.966 | 0.823 | 0.818

The class-wise balanced accuracy across all categories within our dataset
approached 0.9, with the highest accuracy observed for AMD at 0.963 and the
lowest for RVO at 0.885. Similarly, the class-wise recall demonstrated a
comparable pattern, with AMD exhibiting the highest value at 0.966 and RVO
displaying the weakest at 0.789. These notably high metrics, even for
categories with a relatively low number of image samples, suggest that the
selection of image classes is appropriate and the images exhibit robust
features. Concatenation of multiple datasets yielded favorable outcomes: this
approach augmented the variety of diseases within open datasets and enabled
the training of neural networks using images acquired from different OCT
systems. This strategy holds the potential to bolster long-term reliability
and enhance overall classification accuracy.

Further potential applications of the OCTDL dataset include the automated
segmentation of OCT image layers, for which manual segmentation will also be
performed. Labels with pathological conditions are also available in the OCTDL
dataset for every image. Training on both disease and pathological condition
labels with further voting ensembles could also increase classification
accuracy. Semi- and Unsupervised anomaly detection [51] has also been tested
for some diseases and is a promising direction for developing Artificial
Intelligence (AI) in OCT.

The results show that the new OCTDL dataset may be used to support and expand
the application of AI in ophthalmology [52]. The dataset will be extended and
will become more balanced with respect to rare conditions, including inherited
retinal dystrophies and retinopathy of prematurity that may assist with
diagnosing and managing these and related sight-threatening conditions [53].

## Data availability

The datasets used in this study are open-access, with our dataset available at
https://zenodo.org/doi/10.5281/zenodo.10370472,
and https://data.mendeley.com/datasets/sncdhf53xc/1,
Kermany[9] dataset available at
https://data.mendeley.com/datasets/rscbjbr9sj/3,
OCTID[16] dataset available at
https://www.openicpsr.org/openicpsr/project/108503/version/V1/view.

## Code availability

The code used to generate the results in this paper is available at
https://github.com/MikhailKulyabin/OCTDL.

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## Author contributions statement

Data collection, A.N., A.S., A.K.; conceptualization, M.K., A.Z. and A.N.;
software, M.K.; writing-original draft preparation, M.K., A.N., V.B. and M.R.;
writing—review and editing, V.B, M.R. and P.C; supervision, A.M., S.K., A.B.
All authors have read and agreed to the published version of the manuscript.

## Competing interests

The authors declare that they have no known competing financial interests or
personal relationships that could have appeared to influence the work reported
in this paper.