[$\ast$]Corresponding and lead authors with equal contribution;
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# An open-source deep learning algorithm for efficient and fully-automatic
analysis of the choroid in optical coherence tomography

Jamie Burke  School of Mathematics, University of Edinburgh, Edinburgh, UK
Justin Engelmann  School of Informatics, University of Edinburgh, Edinburgh,
UK  Centre for Medical Informatics, University of Edinburgh, Edinburgh, UK
Charlene Hamid  Clinical Research Facility and Imaging, University of
Edinburgh, Edinburgh, UK  Megan Reid-Schachter  Clinical Research Facility and
Imaging, University of Edinburgh, Edinburgh, UK  Tom Pearson  University
Hospital Wales, NHS Wales, Cardiff, Wales, UK  Dan Pugh  British Heart
Foundation Centre for Cardiovascular Science, University of Edinburgh,
Edinburgh, UK  Neeraj Dhaun  British Heart Foundation Centre for
Cardiovascular Science, University of Edinburgh, Edinburgh, UK  Amos Storkey
Institute for Adaptive and Neural Computation, School of Informatics,
University of Edinburgh, Edinburgh, UK  Stuart King  School of Mathematics,
University of Edinburgh, Edinburgh, UK  Tom MacGillivray  Clinical Research
Facility and Imaging, University of Edinburgh, Edinburgh, UK  Centre for
Clinical Brain Sciences, University of Edinburgh, Edinburgh, UK  Miguel O.
Bernabeu  Centre for Medical Informatics, University of Edinburgh, Edinburgh,
UK  The Bayes Centre, University of Edinburgh, Edinburgh, UK  Ian J.C.
MacCormick  Centre for Inflammation Research, The Queen’s Medical Research
Institute, University of Edinburgh, Edinburgh, UK  Institute for Adaptive and
Neural Computation, School of Informatics, University of Edinburgh, Edinburgh,
UK

###### Abstract

Purpose To develop an open-source, fully-automatic deep learning algorithm,
DeepGPET, for choroid region segmentation in optical coherence tomography
(OCT) data.

Methods We used a dataset of 715 OCT B-scans (82 subjects, 115 eyes) from 3
clinical studies related to systemic disease. Ground truth segmentations were
generated using a clinically validated, semi-automatic choroid segmentation
method, Gaussian Process Edge Tracing (GPET). We finetuned a UNet with
MobileNetV3 backbone pre-trained on ImageNet. Standard segmentation agreement
metrics, as well as derived measures of choroidal thickness and area, were
used to evaluate DeepGPET, alongside qualitative evaluation from a clinical
ophthalmologist.

Results DeepGPET achieves excellent agreement with GPET on data from 3
clinical studies (AUC=0.9994, Dice=0.9664; Pearson correlation of 0.8908 for
choroidal thickness and 0.9082 for choroidal area), while reducing the mean
processing time per image on a standard laptop CPU from 34.49s (±15.09) using
GPET to 1.25s (±0.10) using DeepGPET. Both methods performed similarly
according to a clinical ophthalmologist, who qualitatively judged a subset of
segmentations by GPET and DeepGPET, based on smoothness and accuracy of
segmentations.

Conclusions DeepGPET, a fully-automatic, open-source algorithm for choroidal
segmentation, will enable researchers to efficiently extract choroidal
measurements, even for large datasets. As no manual interventions are
required, DeepGPET is less subjective than semi-automatic methods and could be
deployed in clinical practice without necessitating a trained operator.

##  1 Introduction

The retinal choroid is a complex, extensively interconnected vessel network
positioned between the retina and the sclera. The choroid holds the majority
of the vasculature in the eye and plays a pivotal role in nourishing the
retina. Optical coherence tomography (OCT) is an ocular imaging modality that
uses low-coherence light to construct a three-dimensional map of chorioretinal
structures at the back of the eye. Standard OCT imaging does not visualise the
deeper choroidal tissue well as it sits beneath the hyperreflective retinal
pigment epithelium layer of the retina. Enhanced Depth Imaging OCT (EDI-OCT)
overcomes this problem and offers improved visualisation of the choroid, thus
providing a unique window into the microvascular network which not only
resides closest to the brain embryologically, but also carries the highest
volumetric flow per unit tissue weight compared to any other organ in the
body.

Since the advent of OCT, interest in the role played by the choroid in
systemic health has been growing 1, as non-invasive imaging of the choroidal
microvasculature may provide a novel location to detect systemic,
microvascular changes early. Indeed, changes in choroidal blood flow,
thickness and other markers have been shown to correspond with patient health
such as choroidal thickness in chronic kidney disease 2 and choroidal area and
vascularity in Alzheimer’s dementia 3.

Quantification of the choroid in EDI-OCT imaging requires segmentation of the
choroidal space. However, this is a harder problem than retinal layer
segmentation due to poor signal penetration from the device — and thus lower
signal-to-noise ratio — and shadows cast by superficial retinal vessels and
choroidal stroma tissue. This results in poor intra- and inter-rater agreement
even with manual segmentation by experienced clinicians, and manual
segmentation is too labour intensive and subjective to be practical for
analysing large scale datasets.

Semi-automated algorithms improve on this slightly but are typically multi-
stage procedures, requiring traditional image processing techniques to prepare
the images for downstream segmentation 4. Methods based on graph theory such
as Dijkstra’s algorithm 5; 6 or graph cut 7, as well as on statistical
techniques including level sets 8; 9, contour evolution 10, and Gaussian
mixture models 11 have been proposed previously. Concurrently, deep
learning(DL)-based approaches have emerged. 12 used a DL model for choroid
layer segmentation, but with traditional contour tracing as a post-processing
step. Other DL-based approaches, too, combine traditional image processing
techniques as pre- or post-processing steps 13; 14; 15 whereas others are
fully DL-based 16; 17, the latter of which is in a similar vein to the
proposed method. More recently, DL has been used to distil existing semi-
automatic traditional image processing pipelines into a fully-automatic method
18.

Gaussian Process Edge Tracing (GPET), based on Bayesian machine learning 19,
is a particularly promising method for choroid layer segmentation that has
been clinically and quantitatively validated 20. Gaussian process (GP)
regression is used to model the upper and lower boundaries of the choroid from
OCT scans. For each boundary, a recursive Bayesian scheme is employed to
iteratively detect boundary pixels based on the image gradient and the GP
regressor’s distribution of candidate boundaries. However, GPET is semi-
automatic and thus requires time-consuming manual interventions by
specifically trained personnel which introduces subjectivity and limits the
potential for analysing larger datasets or deploying GPET into clinical
practice.

There are currently no accessible, open-source algorithms for fully-automatic
choroidal segmentation. All available algorithms fall into one of three
categories: First, semi-automatic methods 21; 22 that require human
supervision and thus require training and introduce subjectivity. Second,
fully-automatic DL-based methods that are not openly accessible, either only
providing the code but not the trained model necessary to use the method 23 or
not providing any access at the time of writing 24. Third, fully-automatic but
comprising of many steps, requiring a good understanding of image processing
techniques and a license for proprietary software (MATLAB) 25.

We aim to develop and release an open-source, raw image-to-measurement, fully-
automatic method for choroid region segmentation that can be easily used
without special training and does not require licenses for proprietary
software (Fig. 1). Importantly, we intend to not only to make our method
available to the research community, but to do so in a frictionless way that
allows other researchers to download and use our method without seeking our
approval. We distil GPET into a deep learning algorithm, DeepGPET, which can
process images without supervision in a fraction of the time — permitting
analysis of large scale datasets and potential deployment into clinical care
and research practice without prior training in image processing. The code and
model weights for DeepGPET are available here:
https://github.com/jaburke166/deepgpet.

Figure 1: Comparison between the semi-automatic GPET 19; 20 (top) and fully-
automatic DeepGPET (bottom).

##  2 Methods

###  2.1 Study population

We used 715 OCT B-scans belonging to 82 subjects from three studies: OCTANE
26, a study looking at renal function and impairment in chronic kidney disease
patients. i-Test, a study recruiting pregnant women of any gestation or those
who have delivered a baby within 6 months, including controls and individuals
at high risk of complications. Normative, data from 30 healthy volunteers as a
control group 27. All studies conformed with the Declaration of Helsinki and
received relevant ethical approval and informed consent from all subjects.
Table 1 provides an overview of basic population characteristics and number of
subjects/images of these studies. Supplementary Fig. S1 presents box-plot
distributions of choroidal thickness and area for the three datasets used to
build DeepGPET, with Table S1 presenting tabular mean and standard deviation
values.

Two Heidelberg spectral domain OCT SPECTRALIS devices were used for image
acquisition: the Standard Module (OCT1 system) and FLEX Module (OCT2 system).
The FLEX is a portable version that enables imaging of patients in a ward
environment. Both machines imaged a $30^{\circ}$ (8.7 mm) region, generating a
macular, cross-sectional OCT B-scan at $768\times 768$ pixel resolution.
Notably, 14% of the OCT B-scans were non-EDI and thus present more challenging
images with lower signal-to-noise ratio in the choroidal part of the OCT.
Horizontal line and vertical scans were centred at the fovea with active eye
tracking, using an Automatic Real Time (ART) value of 100. Posterior pole
macular scans covered a 30-degree by 25-degree region, using EDI mode.

We split the data into approximately an 85:8:7 split between training (603
B-scans, 66 subjects), validation (58 B-scans, 9 subjects) and test sets (54
B-scans, 7 subjects). When splitting the data, we did so at the patient-level,
i.e. each subjects OCT images are present in only one set, and were selected
so that each set had proportionally equal amounts of scan types (EDI/non-EDI)
to best represent image quality. See supplementary Table S2 for an overview of
basic population and imaging characteristics for each set.

|  OCTANE |  i-Test |  Normative | Total
---|---|---|---|---
Subjects | 47 | 5 | 30 | 82
Male/Female | 24 / 23 | 0 / 5 | 20 / 10 | 44 / 38
Right/Left eyes | 47 / 0 | 5 / 5 | 29 / 29 | 81 / 34
Age (mean (SD)) | 48.8 (12.9) | 34.4 (3.4) | 49.1 (7.0) | 48.0 (11.2)
Machine | Standard | FLEX | Standard | Both
Horizontal/Vertical scans | 166 / 0 | 16 / 16 | 57 / 54 | 239 / 70
Volume scans | 174 | 186 | 46 | 406
Total B-scans | 340 | 218 | 157 | 715
Table 1: Overview of population characteristics. EDI, enhanced depth imaging;
SD, standard deviation.

###  2.2 DeepGPET

As the ground truths are based on GPET, DeepGPET can be can be seen as a more
efficient, fully automatic and distilled version of GPET. Our approach was to
fine-tune a UNet 28 with MobileNetV3 29 backbone pre-trained on ImageNet for
60 epochs with batch size 16 using AdamW 30 ($\text{lr}=10^{-3}$,
$\beta_{1}=0.9$, $\beta_{2}=0.999$, $\text{weight decay}=10^{-2}$). After
epoch 30, we maintain an exponential moving average (EMA) of model weights
which we then use as our final model. We use the following data augmentations:
brightness and contrast changes, horizontal flipping, and simulated OCT
speckle noise by applying Gaussian noise followed by multiplicative noise (all
$p=0.5$); Gaussian blur and random affine transforms (both $p=0.25$). To
reduce memory-load, we crop the black space above and below the OCT B-scan and
process images at a resolution of $544\times 768$ pixels. Images are
standardised by subtracting 0.1 and dividing by 0.2, and no further pre-
processing is done. We used Python 3.11, PyTorch 2.0, Segmentation Models
PyTorch 31 and the timm library 32.

###  2.3 Statistical analysis

We used Dice coefficient and Area Under the ROC Curve (AUC) for evaluating
agreement in segmentations, as well as the Pearson correlation $r$ and Mean
Absolute Error (MAE) for segmentation-derived choroid thickness and area. The
calculation of thickness and area from the segmentation is described in more
detail in 20. Briefly, for thickness the average of 3 measures is used, taken
at the fovea and 2,000 microns from it in either direction by drawing a
perpendicular line from the upper boundary to the lower boundary to account
for choroidal curvature. For area, pixels are counted in a region of interest
3,000 microns around the fovea, which corresponds to the commonly used Early
Treatment Diabetic Retinopathy Study (ETDRS) macular area of $6,000\times
6,000$ microns 33.

We compare DeepGPET’s agreement with GPET’s segmentations against the
repeatability of GPET itself. The creator of GPET, J.B., made both the
original and repeated segmentations with GPET. Since both segmentations were
done by the same person there is no inter-rater subjectivity at play here.
Thus, the intra-rater agreement measured here is a best case scenario and
forms an upper-bound for agreement with the original segmentations and any
other semi-automatic method requiring manual input, which can necessarily be
subject to human variability, unlike DeepGPET.

Method AUC Dice Time (s/img) Thickness Area Pearson $r$ MAE ($\mu$m) Pearson
$r$ MAE (mm2) DeepGPET 0.9994 0.9664 1.25 ± 0.10 0.8908 13.3086 0.9082 0.0699
Repeat GPET 0.9812 0.9672 34.49 ± 15.09 0.9527 10.4074 0.9726 0.0486

Table 2: Metrics for DeepGPET and repeated GPET using the initial GPET
annotation as “ground-truth”. Time given as mean ± standard deviation.

Figure 2: Correlation plots comparing derived measures of mean choroid
thickness (a) and choroid area (b) using DeepGPET and the re-segmentations
using GPET.

In addition to quantitative evaluations, we also compared segmentations by
GPET and DeepGPET for 20 test set OCT images qualitatively by having them
rated by I.M., an experienced clinical ophthalmologist. We selected 7 examples
with the highest disagreement in thickness and area, 7 examples with
disagreement closest to the median, and 6 examples with the lowest
disagreement. Thus, these 20 examples cover cases where both methods are very
different, cases of typical disagreement, and cases where both methods are
very similar. In each instance, I.M. was shown the segmentations of both
methods overlaid on the OCT — blinded to which method produced which
segmentation — and also provided with the raw, full-resolution OCT, and was
then asked to rate each one along three dimensions: Quality of the upper
boundary, the lower boundary and overall smoothness using an ordinal scale:
“Very bad”, “Bad”, “Okay”, “Good”, “Very good”.

##  3 Results

###  3.1 Quantitative

Figure 3: Bland-altman plots comparing the agreement between DeepGPET and GPET
using mean choroid thickness (a) and choroid area (b).

Table 2 shows the results for DeepGPET and a repeat GPET, compared to the
initial GPET segmentation as “ground-truth”.

####  3.1.1 Agreement in segmentation.

Both methods have excellent agreement with the original segmentations.
DeepGPET’s agreement is comparable to the repeatability of GPET itself, with
DeepGPET’s AUC being slightly higher (0.9994 vs 0.9812) and Dice coefficient
slightly lower (0.9664 vs 0.9672). DeepGPET performing better in terms of AUC
but worse in terms of Dice suggests that for pixels where it disagrees with
GPET after thresholding, the confidence is lower than for ones where it agrees
with GPET. This in turn suggests that DeepGPET is well-calibrated based on the
raw predictions made for each pixel.

####  3.1.2 Processing speed and manual interventions.

Both methods were compared on the same standard laptop CPU. DeepGPET processed
the images in only 3.6% of the time that GPET needed. DeepGPET ran fully-
automatic and successfully segmented all images, whereas GPET required 1.27
manual interventions on average, including selecting initial pixels and manual
adjustment of GPET parameters when the initial segmentation failed.

This results in massive time savings: A standard OCT volume scan consists of
61 B-scans. With GPET, processing such a volume for a single eye takes about
35 minutes during which a person has to select initial pixels to guide tracing
(for all images) and adjust parameters if GPET initially failed (for about 25%
of images). In contrast, DeepGPET could do the same processing in about 76
seconds on the same hardware, during which no manual input is needed. DeepGPET
could even be GPU-accelerated to cut the processing time by another order of
magnitude.

The lack of manual interventions required by DeepGPET means that no
subjectivity is introduced unlike GPET, particularly when used by different
people. Additionally, DeepGPET does not require specifically trained analysts
and could be used fully-automatically in clinical practice.

Method Upper boundary Lower boundary Smoothness DeepGPET Very good: 20 Very
good: 4, Good: 10, Okay: 4, Bad: 2 Very good: 5, Good: 12, Okay: 2, Bad: 1
GPET Very good: 20 Very good: 6, Good: 6, Okay: 8, Bad: 0 Very good: 6, Good:
13, Okay: 1, Bad: 0

Table 3: Qualitative ratings of 20 test set segmentations along 3 key
dimensions. The rater was blinded to the identity of the methods and their
order was randomised for every example. Figure 4: Four examples from the
adjudication. The rater preferred DeepGPET for (a–b) and GPET for (c–d). Top
row: green, segmented by both GPET and DeepGPET; red, GPET only; and blue,
DeepGPET only. Bottom row: arrows indicate important choroidal features which
can make segmentation challenging. (a): no large vessels in nasal region to
guide segmentation; (b): lower boundary very faint and below the posterior
most vessels; (c): lower boundary noisy and faint; (d): large suprachoroidal
space visible. Figure 5: An example peripapillary scan from Heidelberg’s
Standard Module, automatically segmented by DeepGPET without manual
intervention.

####  3.1.3 Agreement in choroid area and thickness.

GPET showed very high repeatability for thickness (Pearson $r$=0.9527,
MAE=10.4074 $\mu$m) and area (Pearson $r$=0.9726, MAE=0.0486 mm2). DeepGPET
achieved slightly lower, yet also very high agreement for both thickness
(Pearson $r$=0.8908, MAE=13.3086 $\mu$m) and area (Pearson $r$=0.9082,
MAE=0.0699 mm2). Fig. 2 shows correlation plots for thickness and area.
DeepGPET’s agreement with GPET does not quite reach the repeatability of GPET
itself, when used by the same experienced analyst, but it is quite comparable
and high in absolute terms. Especially noteworthy is that the MAE for
thickness and area is only 21% lower for thickness and 30% lower for area for
repeated GPET than for DeepGPET Thus, DeepGPET comes quite close to optimal
performance, i.e. best case repeatability where the same experienced analyst
did both sets of annotation.

Furthermore, the regression fits in both derived measures for DeepGPET are
closer to the identity line than for the repeated GPET measurements. For CT,
the linear fit estimated a slope value of 1.043 (95% confidence interval of
0.895 to 1.192) and intercept of -7.308 $\mu$m (95% confidence interval of
-48.967 $\mu$m to 34.350 $\mu$m). For CA, the linear fit estimated a slope
value of 1.01 (95% confidence interval of 0.878 to 1.137) and an intercept of
0.016 mm2 (95% confidence interval of -0.195 mm2 to 0.226 mm2). All confidence
intervals contain 1 and 0 for the slope and intercepts, respectively,
suggesting no systematic bias or proportional difference between GPET and
DeepGPET 34; 35.

Fig. 3 shows the residuals between DeepGPET and the ground truth labels from
the held-out test set using Bland-Altman plots 36. Rahman 37 found that intra-
rater agreement and inter-rater agreement of subfoveal choroidal thickness
measurements were 23$\mu$m and 32$\mu$m, respectively. For CT, only 9.3% (5 /
54) were greater than 23$\mu$m in absolute value, with 4 of these representing
major sources of disagreement. Similarly for CA, the majority of residuals
were centred around 0 (mean residual of -0.02mm2), with only 5.5% (3 / 54) of
residuals lying outside the limits of agreement.

###  3.2 Qualitative

Table 3 shows the results of the adjudication between GPET and DeepGPET. The
upper boundary was rated as “Very good” for both methods in all 20 cases.
However, for the lower boundary, DeepGPET was rated as “Bad” in 2 cases for
the lower boundary and 1 case for smoothness. Otherwise, both methods
performed very similarly.

Fig. 4 shows some examples. In (a), DeepGPET segments more of the temporal
region than GPET does, providing a full width segmentation which was preferred
by the rater. Additionally, both approaches are able to segment a smooth
boundary, even in regions with stroma fluid obscuring the lower boundary (red
arrow). In (b), the lower boundary for this choroid is very faint and is
actually below the majority of the vessels sitting most posterior (red arrow).
DeepGPET produced a smooth and concave boundary preferred by the rater, while
GPET fell victim to hugging the posterior most vessels in the subfoveal
region. In (c), DeepGPET rejected the true boundary in the low contrast region
(red arrow) and opted for a more well-defined one, while GPET segmented the
more uncertain path. Since GPET permits human intervention, there is more
opportunity to fine tune it’s parameters to fit what the analyst believes is
the true boundary. Here, the rater preferred GPET, while DeepGPET’s under-
confidence led to under-segmentation and to a bad rating. In (d), the lower
boundary is difficult to delineate due to a thick suprachoroidal space (red
arrow) and thus a lack of lower boundary definition. Here, the rater gave a
bad rating to DeepGPET and preferred GPET, while remarking that GPET actually
under-segmented the choroid by intersecting through posterior vessels. The
choroids in Fig. 4(b–d) are the choroids with the largest CT and CA
disagreement between DeepGPET and GPET as observed in Fig. 3.

##  4 Discussion

We developed DeepGPET, a fully-automatic and efficient method for choroid
layer segmentation, by distilling GPET, a clinically validated semi-automatic
method. DeepGPET achieved excellent agreement with GPET on held-out data in
terms of segmentation and derived choroidal measurements, approaching the
repeatability of GPET itself and well within the threshold expected to exceed
inter-rater agreement as observed in previous work 37. We also found no
significant association between segmentation performance (via Dice score) and
choroidal thickness, area and the Heidelberg signal-to-noise quality index in
the held-out test set (supplementary Table S3 and Fig. S2). Most importantly,
DeepGPET does not require specialist training and can process images fully-
automatically in a fraction of the time, enabling analysis of large scale
datasets and potential deployment in clinical practice.

Figure 6: Three OCT B-scan images from a TopCon imaging device, of which two
were successful (a–b) and one was not (c).

While the observed agreement was very high, it was not perfect. However, even
higher agreement with GPET would not necessarily produce a better method as
GPET itself is not perfect and even conceptually there is debate around the
exact location of choroid-scleral interface (CSI), i.e. the lower choroid
boundary in an OCT B-scan. CSI is commonly defined, e.g. by the original
authors behind EDI-OCT 38, as the smooth inner boundary between the choroid
and sclera, or just below the most posterior vessels but excluding the
suprachoroidal space. However, even that definition is still debated and can
be hard to discern in practice. Not all choroids are smooth, and there are
edge cases like vessels passing from the sclera into the choroid, or stroma
fluid obscurations that make the boundary even more ambiguous. These features,
coupled with low signal-to-noise ratio and vessel shadowing from superficial
retinal vessels, all contribute to the difficult challenge of choroid layer
segmentation.

For quantitative analysis of choroidal phenotypes, the specific definition of
the CSI is secondary to applying the same, consistent definition across and
within patients. Here, fully-automatic methods like DeepGPET provide a large
benefit by removing the subjectivity present in semi-automatic methods. Where
semi-automatic methods require manual input, two analysts with different
understandings of the CSI could produce vastly different segmentations. With
DeepGPET, the same image is always segmented in the same way, removing
subjectivity.

Initial experiments with other types of OCT imaging have positively indicated
DeepGPET’s ability to generalise to different visualisations of the choroid.
Fig. 5 shows a peripapillary scan extracted from the Heidelberg Standard
Module, centred on the optic head, with the choroid automatically segmented.
Fig. 6 shows choroid segmentations using DeepGPET for three OCT B-scans from a
TopCon device (DRI OCT Triton plus) — two cases where DeepGPET works well and
one case where it does not. This shows some promise in it’s usability in scans
different to the Heidelberg macular line scans from which it was trained on.
We hope in future iterations to extend the training data with scans from
different imaging devices and scan locations. We recommend those using
DeepGPET on non-Heidelberg images to review the segmentations after ward as a
sanity check.

In the present work, we used data from three studies, two OCT devices and
included both EDI and non-EDI scans. However, we only used data from subjects
that were either healthy or had systemic but not eye disease, to which
DeepGPET might not be robust to. In future work, we plan to externally
validate DeepGPET and include cases of ocular pathologies. A further
limitation is that while GPET has been clinically validated, not all
segmentations used for training DeepGPET were entirely perfect. Thus,
revisiting some of the existing segmentations and manually improving them to a
“gold standard” for purposes of training the model could improve DeepGPET. For
instance, GPET does not always segment the whole width of the choroid.
Interestingly, DeepGPET already is able to do that in some cases (e.g. Fig.
4(a) and Fig. 5), but also does emulate the incomplete segmentations by GPET
in other cases. A model trained on enhanced “gold standard” segmentations
would produce even better segmentations.

Finally, we have focused on segmentation as it is the most important and most
time-consuming step of choroidal analysis. However, the location of the fovea
on OCT images needs identified to define the region of interest for derived
measurements such as thickness, area and volume. Identifying the fovea is less
time-consuming or ambiguous than choroid segmentation, and so we plan to
extend DeepGPET to output the fovea location. This would make DeepGPET a fast
and efficient end-to-end framework capable of converting a raw OCT image to a
set of clinically meaningful segmentation-derived measurements. Likewise,
segmenting the choroidal vessels is a very challenging task even for humans
and would be prohibitively time-consuming to do manually, but in the future we
aim to explore whether DeepGPET can automatically segment the vasculature
within the choroid as well.

##  5 Conclusion

Choroid segmentation is a key step in calculating choroidal measurements like
thickness and area. Currently, this is commonly done manually which is labour
intensive and introduces subjectivity. Semi-automatic methods only partially
alleviate both of these problems, and previous fully-automatic methods were
not easily accessible for researchers. DeepGPET addresses this gap as a fully-
automatic, end-to-end algorithm that does not require manual interventions.
DeepGPET provides similar performance as the previously clinically validated,
semi-automatic GPET, while being fully-automatic and an order of magnitude
faster. This enables the analysis of large scale datasets and potential
deployment in clinical practice without necessitating a trained operator.
Although the definition of the lower choroid boundary is still subject to
debate - especially when it comes to suprachoroidal spaces - the most
important consideration is to have a method that consistently applies the same
definition across subjects and studies, which DeepGPET as a fully-automatic
method provides. As an easily accessible, open-source algorithm for choroid
segmentation, DeepGPET will enable researchers to easily calculate choroidal
measurements much faster and with less subjectivity than before.

## Acknowledgements

J.B. was supported by the Medical Research Council (grant MR/N013166/1) as
part of the Doctoral Training Programme in Precision Medicine at the Usher
Institute, University of Edinburgh. J.E. was supported by UK Research and
Innovation (grant EP/S02431X/1) as part of the Centre of Doctoral Training in
Biomedical AI at the School of Informatics, University of Edinburgh. For the
purpose of open access, the authors have applied a creative commons
attribution (CC BY) licence to any author accepted manuscript version arising.
The authors would also like to thank all participants in the studies used in
this paper, as well as all staff at the Edinburgh Imaging Facility who
contributed to image acquisition for this study. We would like to thank Diana
Moukaddem, Niall Strang, Lyle Gray (Glasgow Caledonian University) and Paul
McGraw (University of Nottingham) for providing the three TopCon OCT B-scan
images.

##  6 Conflicts of Interest

The authors declare no conflicts of interest.

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## Supplementary Material

Figure S1: Box-plot distribution plots of choroid thickness (left) and area (right) of the three datasets, OCTANE, i-Test and Normative. |  OCTANE |  i-Test |  Normative
---|---|---|---
Choroid thickness ($\mu$m) |  220.4 (68.6)${}^{\text{**,***}}$ |  245.3 (89.4)${}^{\text{**}}$ |  256.8 (65.9)${}^{\text{***}}$
Choroid area (mm2) |  1.19 (0.37)${}^{\text{*,**}}$ |  1.30 (0.50)${}^{\text{*}}$ |  1.32 (0.35)${}^{\text{**}}$
Table S1: Choroidal characteristics of the three datasets used to build DeepGPET. Values are shown as mean with standard deviation values in parenthesis. Statistically significant results were computed using the T-test comparing the means of independent samples; * : P$<0.01$, ** : P$<0.001$, *** : P$<0.0001$. |  Training |  Validation |  Testing | Total
---|---|---|---|---
Subjects | 66 | 9 | 7 | 82
Male/Female | 32 / 34 | 7 / 2 | 5 / 2 | 44 / 38
Right/Left eyes | 66 / 27 | 9 / 4 | 6 / 3 | 81 / 34
Standard/FLEX Device | 63 / 3 | 8 / 1 | 6 / 1 | 77 / 5
EDI/non-EDI | 51 / 15 | 6 / 3 | 5 / 2 | 62 / 20
Age (mean (SD)) | 49.2 (11.4) | 40.2 (8.2) | 44.9 (8.2) | 48.0 (11.2)
OCTANE cohort | 38 | 5 | 4 | 47
i-Test cohort | 3 | 1 | 1 | 5
Normative cohort | 25 | 3 | 2 | 30
Horizontal/Vertical scans | 202 / 57 | 19 / 8 | 18 / 5 | 239 / 70
Volume scans | 344 | 31 | 31 | 406
Total B-scans | 603 | 58 | 54 | 715
Table S2: Overview of population and image characteristics of the training, validation and test sets. | Pearson | Spearman
---|---|---
| $r_{p}$ |  P-value | $r_{s}$ |  P-value
Choroid thickness | 0.22 | 0.11 | 0.08 | 0.57
Choroid area | 0.23 | 0.09 | 0.12 | 0.37
Quality | -0.11 | 0.43 | 0.001 | 0.99
Table S3: Pearson and Spearman correlation coefficients between Dice scores
against Heidelberg-measured quality index, choroid thickness and area in the
held-out test set between DeepGPET and ground truths generated from GPET.
Figure S2: Test set Dice scores plotted against choroid thickness (left), area
(middle) and Heidelberg-measured quality index (right) in the held-out test
set. The outlier Dice score of approximately 0.84, is the dice score between
DeepGPET and GPET from figure 4(d).