Shedding Light on Early Central Nervous System Changes for

Alzheimer’s Disease through the Retina: An Animal Study

Rui Bernardes

1,2 a

, Hugo Ferreira

1 b

, Pedro Guimar

aes

1 c

and Pedro Serranho

1,3 d

Coimbra Institute for Biomedical Image and Translational Research, Faculty of Medicine, University of Coimbra,

Edif

ıcio do ICNAS, Polo 3 Azinhaga de Santa Comba 3000-548 Coimbra, Portugal

University of Coimbra, CACC - Clinical Academic Center of Coimbra, Faculty of Medicine (FMUC), Coimbra, Portugal

Department of Science and Technology, Universidade Aberta, Rua da Escola Polit

ecnica, 147, 1269-001 Lisbon, Portugal

Keywords:

Optical Coherence Tomography, Retina, Biomarkers, Texture, Convolutional Neural Network, Alzheimer’s

Disease, Mouse Model, Diagnosis.

Abstract:

The World Health Organization (WHO) 2015 projections estimated 75.6 million people living with dementia

in 2030, an update from the 66 million estimated in 2013. These ﬁgures account for all types of dementia, but

Alzheimer’s disease stands out as the most common estimated type, representing 60% to 80% of the cases.

An increasing number of research groups adopted the approach of using the retina as a window to the brain.

Besides being the visible part of the central nervous system, the retina is readily available through non-invasive

imaging techniques such as optical coherence tomography (OCT). Moreover, cumulative evidence indicates

that neurodegenerative diseases can also affect the retina. In the work reported herein, we imaged the retina of

wild-type and the triple-transgenic mouse model of Alzheimer’s disease, at the ages of one-, two-, three-, four-,

eight-, twelve- and sixteen-months-old, by OCT and segmented gathered data using a developed convolutional

neural network into distinct layers. Group differences through texture analysis of computed fundus images for

ﬁve layers of the retina, normative retinal thickness data throughout the observation period of the ageing

mice, and ﬁndings related to the estimation of the ageing effect of the human genes present in the transgenic

group, as well as the classiﬁcation of individual fundus images through convolutional neural networks, will be

presented and thoroughly discussed in the Special Session on ”New Developments in Imaging for Ocular and

Neurodegenerative Disorders”.

1 INTRODUCTION

A biomarker of dementia from a non-invasive, widely

available and inexpensive imaging technique is of ut-

most importance. Biomarkers can provide insights

into the underlying pathophysiology and may be used

as inclusion criteria and outcome measures for clin-

ical trials (Ahmed et al., 2014). Also, the technique

should not be based on the loss of tissue (since it oc-

curs in the late stages of the disease) and have the

property of not being speciﬁc for the type of demen-

tia. Such a technique is of paramount importance to

shed light on the changes unfolding in the retina.

Even though the number of people with dementia

is increasing rapidly (Alzheimer’s & Dementia, 2020)

https://orcid.org/0000-0002-6677-2754

https://orcid.org/0000-0003-1199-8489

https://orcid.org/0000-0002-9465-4413

https://orcid.org/0000-0003-2176-3923

and several research groups focused on this signiﬁcant

problem using the eye as a window to the brain, two

major issues persist with the non-invasive followed

approaches to date. First, research groups focused

their attention on the measurement of the thickness

of the retinal nerve ﬁbre layer (RNFL) of the imaged

retina and reported the thinning of the RNFL, even

though with inconsistent results. Moreover, this thin-

ning occurs in the late stages of the disease, mak-

ing it useless as a screening technique. Second, the

most common form of dementia –Alzheimer’s dis-

ease (AD)– can remain undiagnosed for years (Habib

et al., 2017; Krantic and Torriglia, 2014) and can

only be deﬁnitely diagnosed post-mortem (Krantic

and Torriglia, 2014), despite that ”...the degree of at-

rophy on MRI correlates well with Braak staging at

autopsy.” (Jack et al., 2010) (MRI – magnetic reso-

nance imaging).

Both these problems have the same drawback:

Bernardes, R., Ferreira, H., Guimarães, P. and Serranho, P.

Shedding Light on Early Central Nervous System Changes for Alzheimer’s Disease through the Retina: An Animal Study.

DOI: 10.5220/0011125600003209

In Proceedings of the 2nd International Conference on Image Processing and Vision Engineering (IMPROVE 2022), pages 247-258

ISBN: 978-989-758-563-0; ISSN: 2795-4943

247

used biomarkers are valid only in the late stages of

the disease. Thus, none can determine its onset when

therapeutic intervention may have effective results on

its progression (Hart et al., 2016). These drawbacks

call for a distinct approach from the current and fore-

seen ones (Ahmed et al., 2014).

We took the initial steps towards a potential so-

lution by conducting exploratory tests using a novel

approach to tackle the above problems. Using a su-

pervised machine learning algorithm – support vector

machine (SVM) – we explored the hypothesis that the

human retina’s optical coherence tomography (OCT)

data embeds information on neurodegeneration not

perceivable by direct observation. The underlying ra-

tionale is the sensitive nature of OCT to subtle refrac-

tive index changes and the amount of data gathered

from the ocular fundus. Even though minor differ-

ences cannot be individually detected, they may still

inﬂuence the statistics of the global data. Prelimi-

nary studies showed the possibility of discriminating

healthy controls by age group and discriminating be-

tween healthy controls and patients diagnosed with

Alzheimer’s, Multiple Sclerosis and Parkinson’s dis-

ease (Nunes et al., 2019), thus conﬁrming our work-

ing hypothesis and rationale.

Keeping in mind the second problem, that AD

can only be conﬁrmed post-mortem, the alterna-

tive comes from the triple-transgenic mouse model

of Alzheimer’s disease (3×Tg-AD) (Oddo et al.,

2003). It develops age-dependent amyloid-beta and

tau pathologies, activated microglia, loss of synapses

and neurodegeneration (Kitazawa et al., 2012).

Therefore, in the work herein reported, we rely

on the 3×Tg-AD to identify the very early signs

of disease and monitor the evolution of these signs.

This approach allows us to determine which struc-

tures (layers) of the retina will be affected, their rela-

tive levels of change to the healthy condition and the

sequence of events, that is, which structures show dif-

ferences to the healthy state and in which chronolog-

ical order.

Our preliminary data using healthy controls (WT

– wild-type) and 3×Tg-AD mouse model imaged

by an OCT allowed concluding that the results are

consistent despite the human/mouse biological differ-

ences and the differences in instrumentation. It is pos-

sible to discriminate between healthy and unhealthy

subjects in both cases. These ﬁndings pave the way to

study further the changes over time from the animal

model of disease and look for similar changes in avail-

able data from the human retina, considering different

pathologies and healthy controls. Interestingly, even

though studies on the central nervous system (CNS)

in animals do not commonly readily translate to hu-

mans, our data suggest a parallelism between OCT

data from animals and humans.

The works of La Morgia et al. (Morgia et al.,

2016) and Sch

on et al. (Sch

on et al., 2012) show, re-

spectively, the accumulation of amyloid-beta deposits

in and around melanopsin retinal ganglion cells and

the presence of hyperphosphorylated tau in the inner-

most layers of the retina from AD patients. These

ﬁndings support the hypothesis that the analysis of

fundus projection images computed from OCT data

(see (Guimar

aes et al., 2014)) may embed useful in-

formation on the content of the retina concerning

CNS changes.

The texture analysis of the computed fundus im-

ages for distinct layers of the mouse retina revealed

differences in texture between the WT and the 3×Tg-

AD groups and the nature of these differences over

time, suggesting a cumulative effect of the ageing and

that of the disease’s associated genes for the trans-

genic mice group.

Despite this exploratory approach, achieved re-

sults strongly suggest the presence of useful infor-

mation on the status of the CNS within the com-

puted fundus references, in agreement with (Morgia

et al., 2016; Sch

on et al., 2012). The importance of

such a ﬁnding is ﬁvefold. First, texture-based met-

rics may provide additional information to strengthen

the biomarker of CNS neurodegeneration. Second, it

may lead to discrimination between different forms

of neurodegeneration. Third, it may lead to a very

early biomarker of dementia and, in consequence,

shed light on the pathogenesis of the disease. Fourth,

it may identify the window for treatment before a late-

stage where treatment of CNS degeneration is ineffec-

tive and, ﬁfth, for the efﬁcient monitoring of therapy

efﬁcacy.

Despite the hurdles, we could conﬁrm, over time

and using different populations of controls and pa-

tients, that gathered data from the ocular fundus em-

beds valuable information on the human CNS. Fur-

thermore, using WT and the triple-transgenic mouse

model of AD and different instrumentation, we could

replicate ﬁndings in humans and mice solely based on

OCT data of the retina, bridging the gap between hu-

man and animal ﬁndings on the CNS status, based on

the fact that the exploratory results are consistent in

humans and mice. This bridge is of high interest by

opening the possibility of developing biomarkers of

AD using non-invasive and widely available instru-

mentation.

The independent analysis of the distinct retinal

layers calls for the segmentation of OCT data, where

the images are divided into regions corresponding to

retinal layers or layer-complexes and the background

Imaging4OND 2022 - Special Session on New Developments in Imaging for Ocular and Neurodegenerative Disorders

248

(vitreous and choroid). Over time, several approaches

have been proposed to address this challenging prob-

lem since the introduction of the OCT in clinical prac-

tice, starting with time-domain systems, when the res-

olution was only a fraction of the current one.

Current methods can be divided into mathematical

methods and machine learning approaches. Mathe-

matical methods encode all retinal information known

a priori into mathematical models towards the seg-

mentation. These models are, however, difﬁcult to

obtain, and even more so to capture variations from

different mice strains and various pathologies, primar-

ily where signiﬁcant differences in the structure ap-

pearance of the retina (Berger et al., 2014; Soukup

et al., 2019) and signiﬁcant differences in image con-

trast are found within the same B-scan, across differ-

ent B-scans, and eyes.

The mathematical approaches aiming to detect

retinal interfaces bounding the respective retinal lay-

ers include Markov modelling (Koozekanani et al.,

2021), active contour modelling (Yazdanpanah et al.,

2011), variational methods (Rathke et al., 2014), and

graph-based methods (Chiu et al., 2010; Dufour et al.,

2013; Srinivasan et al., 2014; Fang et al., 2017).

In particular, graph-based approaches have shown

promising results for the segmentation of several reti-

nal interfaces. However, graph-based techniques typ-

ically use constraints to achieve better segmentation

performances (Dufour et al., 2013). Unfortunately,

while these constraints help produce good results for

the datasets over which the methods were developed,

these methods are tuned for a particular dataset. Fur-

thermore, these methods typically make use of pre-

processing (e.g. denoising (Srinivasan et al., 2014))

and post-processing steps, as well as heuristics, to

achieve reported results (Chiu et al., 2010). More

recently, a combination of graph-based methods and

machine learning has been explored, where the ma-

chine learning output is used as the basis for the

graph-search method (Fang et al., 2017).

Machine learning approaches address the problem

distinctly. These typically employ pixel classiﬁcation

techniques, either by classifying each pixel as belong-

ing to a layer (semantic segmentation) or a bound-

ary (interface detection). Deep learning methods have

gained momentum in general computer vision appli-

cations and eye research (Sarhan et al., 2020), espe-

cially convolutional neural networks (CNN). Over the

last few years, deep learning approaches have been

proposed to solve the retinal segmentation problem

(Roy et al., 2017; Antony et al., 2017; Ngo et al.,

2020; Venhuizen et al., 2017). However, despite

achieved results, most published works in this ﬁeld

address the human retina, therefore not suitable for

mice due to the vast differences in the retinal struc-

ture between human and rodent retinas.

While there is a growing body of work on deep

learning applied to rodents’ OCT data, these mod-

els are generally trained on a narrow age range and

use a single mice strain/pathology (Roy et al., 2017;

Morales et al., 2021). Consequently, age-related

changes, both in healthy ageing and potentially more

so in neurodegenerative diseases, like Alzheimer’s

disease (Chiu et al., 2012; London et al., 2013), are

not considered, again restricting the applicability of

these methods to a limited strain, age range, and con-

dition.

In this work, we make use of a deep learning

process to address the identiﬁcation of 10 image re-

gions (7 retinal layers, 1 layer-complex, vitreous,

and choroid) while imposing the following constraints

into the process: a neural network as small as pos-

sible aiming the minimum number of parameters,

no signiﬁcant pre-processing (e.g. no denoising re-

quired), take the known organisation into account and

cope with a wide range of ages, mice strains, healthy

and pathological cases (diabetes and neurodegenera-

tive cases). We leave out anatomical abnormalities

like cystoids, retinal detachments, holes, and similar

changes in the retina, as these are not within the scope

of our research.

2 MATERIALS AND METHODS

2.1 Data

2.1.1 Ethics Statement

This study was approved by the Animal Welfare

Committee of the Coimbra Institute for Clinical and

Biomedical Research (iCBR), Faculty of Medicine,

University of Coimbra, and by Direc¸

ao-Geral de

Alimentac¸

ao e Veterin

aria (DGAV). All procedures

involving mice were conducted as per the Associa-

tion for Research in Vision and Ophthalmology state-

ment for animal use, and in agreement with the Eu-

ropean Community Directive Guidelines for the care

and use of nonhuman animals for scientiﬁc purposes

(2010/63/EU), transposed into the Portuguese law in

2013 (DL113/2013).

2.1.2 Mouse Characterisation

In this study, 57 mice from each strain, C57BL6/129S

and 3×Tg-AD, were used, at the ages of one to four

months old and at eight, twelve and sixteen months

old, and considering both left and right eyes. All

Shedding Light on Early Central Nervous System Changes for Alzheimer’s Disease through the Retina: An Animal Study

249

mice were housed and maintained at the vivarium of

iCBR, Faculty of Medicine, University of Coimbra,

and were on a 12-h light/dark cycle with free access

to both food and water.

2.1.3 Experimental Setup

Concerning OCT imaging preparation, mice were

anaesthetised using a mixture of 80 mg/kg of ke-

tamine and 5 mg/kg of xylazine. The pupils were di-

lated using a solution of 0.5% tropicamide and 2.5%

phenylephrine. Additionally, oxibuprocaine, a local

anaesthetic, was used. Eyes were regularly lubricated

using eye drops (1% carmellose).

All retinas were imaged by a Micron IV OCT

System (Phoenix Technology Group, Pleasanton, CA,

USA). It creates a volume per acquisition composed

of 512 B-scans, each with 512 A-scans of 1024 val-

ues in length; B-scans are saved as a non-compressed

TIFF ﬁle image.

The system presents an imaging depth of 1.4 mm

and an axial resolution of 3 µm, as determined by the

superluminescent diode’ bandwidth and central wave-

length, 160 and 830 nm, respectively. All scans were

taken by the same operator in the same retinal region

using the optic disc as a landmark, centred horizon-

tally with the optic disc and vertically above it.

2.2 Segmentation Neural Network

The neural network for the retina segmentation builds

upon the U-Net (Ronneberger et al., 2015). This

fully convolutional neural network combines local

and global information to create a more accurate im-

age segmentation process. This architecture consists

of two symmetric paths, an encoder path, where the

size of the feature maps is reduced to capture the

global information, and a decoder path, where feature

maps produced by the encoder are upscaled to match

the input image size. In addition, skip-connections

were also used to transfer features maps from the en-

coding path to the respective level of the decoding

path (Figure 1), which allows achieving better data lo-

calisation. Finally, the classiﬁcation block allows for

semantic segmentation from the feature maps, where

classes represent retinal layers/layer-complex, vitre-

ous, and choroid, towards identifying classes’ bound-

aries (interfaces).

Furthermore, the proposed architecture uses resid-

ual learning (He et al., 2016; Zhang et al., 2018;

Jha et al., 2019; Diakogiannis et al., 2020). Resid-

ual learning has improved neural network learning by

helping with derivative propagation during the train-

ing process (He et al., 2016).

All convolutions made use of stride 1 and kernel

3×3 in size. Also, zero-padding was used to preserve

the dimensions of feature maps in each convolutional

layer. Batch Normalisation (BN) and ReLu (Rectiﬁed

Linear Unit) were used to prevent over-ﬁtting of the

neural network (Ioffe and Szegedy, 2015) and intro-

duce non-linearity in the training process. For details

on the neural network, see Table 1.

Figure 1: Semantic segmentation neural network scheme

employed to segment OCT’s B-scans.

2.2.1 Encoding / Decoding Blocks

Each block of the encoding path starts with a resid-

ual learning block, followed by a convolution, BN

and ReLu activation layers (Figure 1). During this

process, the number of channels is doubled. Finally,

the encoding block ends with a max-pooling layer

to halve feature maps’ size, using a 2×2 kernel and

stride 2.

The decoding blocks start with the upscaling of

the respective feature maps, combined with the fea-

ture maps from the corresponding encoding level.

The remaining steps are similar to the encoding

blocks. First, a residual block is used, followed by

a convolution, BN and ReLu activation layer (Fig-

ure 1). In each decoding/encoding block, the number

of channels is halved/doubled.

The classiﬁcation is achieved using a convolu-

tional layer with a 1 × 1 kernel and reduces the num-

ber of channels to match the number of classes (10),

Imaging4OND 2022 - Special Session on New Developments in Imaging for Ocular and Neurodegenerative Disorders

250

Table 1: Network structure.

Unit Level Layer / Block Filter (Kernel / channels) Stride Output Size

Input 768 x 512 x 1

Encoder Level 1 Residual 3x3 / 16 1 768 x 512 x 16

Conv 3x3 / 16 1 768 x 512 x 16

Level 2 Residual 3x3 / 32 1 384 x 256 x 32

Conv 3x3 / 32 1 384 x 256 x 32

Level 3 Residual 3x3 / 64 1 192 x 128 x 64

Conv 3x3 / 64 1 192 x 128 x 64

Level 4 Residual 3x3 / 128 1 96 x 64 x 128

Conv 3x3 / 128 1 96 x 64 x 128

Level 5 Residual 3x3 / 256 1 48 x 32 x 256

Conv 3x3 / 256 1 48 x 32 x 256

Decoder Level 5 Residual 3x3 / 256 1 48 x 32 x 256

Conv 3x3 / 256 1 48 x 32 x 256

Level 4 Residual 3x3 / 128 1 96 x 64 x 128

Conv 3x3 / 128 1 96 x 64 x 128

Level 3 Residual 3x3 / 64 1 192 x 128 x 64

Conv 3x3 / 64 1 192 x 128 x 64

Level 2 Residual 3x3 / 32 1 384 x 256 x 32

Conv 3x3 / 32 1 384 x 256 x 32

Level 1 Residual 3x3 / 16 1 768 x 512 x 16

Conv 3x3 / 16 1 768 x 512 x 16

Output Conv 1x1 / 10 1 768 x 512 x 10

Output 768 x 512 x 1

followed by a softmax activation.

2.2.2 Loss Function

The cross-entropy loss function is used (eq. 1) where

ω is the weigh function (with custom weight per

pixel), g

is the ground truth for class l, and p

is the

neural network prediction for class l. This weighting

scheme aims two-fold: to produce accurate classiﬁ-

cations near interface locations; and reduce the likeli-

hood that misclassiﬁcations occur near the interfaces.

The second goal is of particular interest as it is

not intended to boost efﬁciency. Indeed, its contri-

bution in this regard may be negligible. However, it

helps the overall method by reducing the number of

classiﬁcation errors close to interfaces that would be

difﬁcult, if not impossible, to solve with further pro-

cessing. On the other hand, errors away from inter-

faces can be solved based on a priori information on

the retina, namely that each layer is present (manda-

tory) for the entire B-scan and that layers are strictly

stratiﬁed.

cross

= −

∑

ω(x, l)g

(x)log(p

(x)). (1)

The weight function (ω) is composed of two

components: one captures the differences between

classes, and another differentiates the relevance of

pixels within the same class. The ﬁrst component is

the class weights determined by the inverse of the rel-

ative frequency of each class



Γ(l) =

f (l)



The second component uses the distance to the

nearest interface to calculate a weight per pixel. As

determined above, this gives higher weights for pix-

els closer to interfaces. This weight is computed by:

α(x, l) = e

−

d(x,l)

, (2)

where d(x, l) is the distance to the nearest interface,

and σ = 15 as determined based on the thickness of

the thinnest layer. The weight function was built such

that the pixel weight is added as a factor for the class

weight:

ω(x, l) = Γ(l)(1 + α(x, l)). (3)

2.3 Datasets

Mice were separated into two sets, one for training

and validation (N=96) and one for testing (N=18),

each with a balanced distribution of WT and 3×Tg-

AD mice. This mice-based split ensures the complete

separation between data used for training and testing.

The training set was built by randomly consider-

ing 25 volumes from each group and the ﬁrst four

time-points. From each volume, 15 B-scans were ran-

domly selected, with a minimum separation of 5 B-

scans to reduce the similarities between selected B-

Shedding Light on Early Central Nervous System Changes for Alzheimer’s Disease through the Retina: An Animal Study

251

scans, yielding a training set of 3000 B-scans (25 vol-

umes × 2 groups × 4 time points × 15 B-scans).

Horizontally mirroring and sinusoidal vertical dis-

placements were used towards data augmentation to

reduce over-ﬁtting and introduce further variability

in the used dataset. The latter uses a sine function

(t(x) = K sin(2π f x + φ)) to determine the vertical

translation of each A-scan across the B-scan. In this

work, we used two sets of range for two of the param-

eters, K and f , one focusing on low amplitude and

high frequency distortions and one focusing on high

amplitude and low frequency. These parameters were

randomly set for each B-scan in the training dataset.

The phase, φ, was not constrained in any way. The

resulting images and the original B-scan example are

shown in Figure 2.

Figure 2: Example of data augmentation, from left to right:

original B-scan, mirrored B-scan, sinusoidal displacements,

respectively low amplitude/high frequency and high ampli-

tude/low frequency.

The ground truth used to train a neural network

is often provided by experts’ manual segmentation

of images composing the training set. However, this

is not a viable option for the current number of im-

ages because of the burden, the time required, and all

associated costs. Hence, a semi-automated process

was used where segmentations were performed based

on image and signal processing techniques (Ferreira

et al., 2020), ﬁrst and then validated and corrected,

wherever required, by experts.

From the split into training and validation sets (see

above), 180 volumes (92 160 B-scans — 18 mice)

composed the test set.

These volumes were segmented by the developed

neural network (Figure 1) to produce ﬁve mean value

fundus images (MVF) (Guimar

aes et al., 2014), each

corresponding to a distinct anatomical layer/layer-

complex of the retina, speciﬁcally the retinal nerve

ﬁbre and ganglion cell layer complex (RNFL-GCL),

the inner plexiform layer (IPL), the inner nuclear

layer (INL), the outer plexiform layer (OPL), and the

outer nuclear layer (ONL).

2.4 Training

The Adam method (Kingma and Ba, 2015) was used

during training using a batch size of 8. The initial

learning rate was set to 10

−3

and subsequently re-

duced by a factor of 0.75 when error plateaus. Early

stopping was used to prevent over-ﬁtting, and both

learning rate and early stopping used the validation

accuracy as the performance metric.

All processing was done in Python 3.7.9, with the

neural network models using the Keras 2.7 (Chollet,

2015) framework with TensorFlow 2.7 as backend

(Abadi et al., 2016). The training was performed in

a Ubuntu desktop, equipped with an AMD Rysen 9

3900x CPU @3.8 GHz with 12 cores, 64 GB RAM,

and an Nvidia RTX 3060 with 12 GB of memory, us-

ing version 11.5 of CUDA.

2.5 Interface Detection

The CNN proposed herein produces the semantic seg-

mentation of a single B-scan towards the ultimate

goal of determining the interfaces between retinal

layers/layer-complexes, as shown in Figure 3. A vali-

dation step was implemented at the level of individual

A-scans, and an A-scan is considered correctly seg-

mented if fulﬁlling the following criteria:

1. all layers are present;

2. any layer can only appear once;

3. the order of the layers is ﬁxed;

4. each layer thickness is consistent across all A-

scans of this B-scan.

These criteria are possible because the optic disc is of

no interest for the present study and only retinas close

to the healthy status are of interest. Therefore, thick-

ness’ variability throughout the B-scan is expected to

be low to all layers.

2.6 Texture Analysis

A MVF was computed for each of the segmented

layers/layer-complexes (Figure 4), with texture anal-

ysis being carried out based on the grey-level co-

occurrence matrices (GLCM) (Haralick et al., 1973)

after reducing the number of grayscale levels from

65536 (16 bits) to 16 (4 bits). Twenty features were

determined per block as in (Soh and Tsatsoulis, 1999)

and (Haralick et al., 1973), and direction. Further-

more, four directions (0º, 45º, 90º and 135º) with sym-

metry ON and scale (pixel distance) of one pixel led

to four GLCM per block (24 × 24 blocks of 20 × 20

pixels each). Each feature was then considered the

Imaging4OND 2022 - Special Session on New Developments in Imaging for Ocular and Neurodegenerative Disorders

252

Figure 3: Semantic segmentation (left) and corresponding

interface detection (right) for an example B-scan.

maximum across the four directions to decouple fea-

tures’ values from the orientation, resulting in 20 fea-

tures per block.

Figure 4: Mean value fundus images of a triple-transgenic

mouse model of Alzheimer’s disease. From left to right and

top to bottom: retinal nerve ﬁbre layer-ganglion layer com-

plex; inner plexiform layer; inner nuclear layer; outer plex-

iform layer, and; outer nuclear layer.

3 RESULTS

3.1 Segmentation Capability

In addition to the performance of the segmentation,

concerning the ground truth segmentation, it is es-

sential to assess the feasibility of the segmentation

process to deliver results for a signiﬁcant number of

cases. This work deals with retinas with signiﬁcantly

different appearances due to the study duration. In-

deed, the retina of a mouse at one-month-old does

look signiﬁcantly different from that at twelve or six-

teen months old.

The histogram of the fraction of A-scans cor-

rectly segmented, following the criteria outlined in

section 2.5, is presented in Figure 5. Here, 163 of

the 180 volumes of the test set present 90% to 100%

of A-scans with valid segmentations at all layers.

Figure 5: Histogram of the fraction of A-scans with valid

segmentations at all layers.

A threshold was established from the histogram

above, and only data from volumes with at least 90%

of A-scans correctly segmented were considered. Ta-

ble 2 presents the fraction of these volumes from the

test set by group, eye and age. The number of vol-

umes on the test set varies because of the exclusion of

data due to image quality and the death of mice during

the studied period.

Overall, the segmentation works well in all cases.

However, two particular instances stand out at three

and sixteen months old, both for the control group.

These volumes belong to the same three mice in both

cases. This fact suggests that these consistent errors

are mice speciﬁc rather than a segmentation problem.

The texture analysis found extensive and statisti-

cally signiﬁcant differences between groups.

3.2 Thickness

3.2.1 Normative Data

A normative database was generated based on the

512 × 512 thickness value maps by computing the av-

erage and standard deviation for 3× 3 blocks. Indeed,

a normative 3 × 3 thickness map was computed per

group and time point. An example of a normative

map is provided in Figure 6, along with two appli-

cation examples. A detailed and thorough analysis of

the normative database will be provided in a subse-

quent publication.

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253

Table 2: Fraction of volumes of the test set correctly segmented by group, eye and age.

Group Eye

One Two Three Four Eight Twelve Sixteen

Month Months Months Months Months Months Months

9 / 9 6 / 6 6 / 6 6 / 6 5 / 6 6 / 6 5 / 6

(100%) (100%) (100%) (100%) (83.3%) (100%) (83.3%)

8 / 9 6 / 6 3 / 6 6 / 6 6 / 6 5 / 6 3 / 6

(88.9%) (100%) (50%) (100%) (100%) (83.3%) (50%)

Total

17 / 18 12 / 12 9 / 12 12 / 12 11 / 12 11 / 12 8 / 12

(94.4%) (100%) (75%) (100%) (91.7%) (91.7%) (66.7%)

8 / 8 7 / 9 8 / 8 5 / 6 6 / 6 5 / 5 3 / 3

(100%) (77.8%) (100%) (83.3%) (100%) (100%) (100%)

8 / 8 7 / 8 8 / 8 5 / 6 6 / 7 4 / 5 3 / 3

(100%) (87.5%) (100%) (83.3%) (85.7%) (80%) (100%)

Total

16 / 16 14 / 17 16 / 16 10 / 12 12 / 13 9 / 10 6 / 6

(100%) (82.4%) (100%) (83.3%) (92.3%) (90%) (100%)

Figure 6: Left: normative thickness values (µm) for the outer nuclear layer (ONL) of WT mice (two-months-old). Thickness

maps for a WT and a 3×Tg-AD mouse (both two-months-old). WT – wild-type; 3×Tg-AD – triple-transgenic mouse model

of Alzheimer’s disease.

3.2.2 Group Differences

The comparison between groups shows consistent

retinal layer differences with the 3×Tg-AD group

presenting a decreased layer thickness except for the

RNFL-GCL complex, where the 3×Tg-AD group

presents an increased thickness, and the ONL, where

thickness values match one another. This ﬁnding (at

the RNFL-GCL complex) is against common results

reporting decreased RNFL thickness. Nevertheless,

in the present study, two layers aggregated are consid-

ered because, in these animal groups, it is not possible

to distinguish the RNFL from the GCL.

3.2.3 Age Differences

We demonstrated different behaviours for the retina’s

layers with ageing based on collected data. While

some get thicker over time, some get thinner, and

some keep the same for the reported period, estab-

lishing a thickness normative database (Ferreira et al.,

2021) for each layer, location and time point that can

be used for other comparative studies. A thorough

analysis will be the subject of a subsequent publica-

tion.

3.3 Texture Analysis

From MVF images computed per retinal layer/layer-

complex, as shown in Figure 4, texture analysis met-

rics were computed as disclosed in section 2.6. Four

retinal fundus regions (quadrants) were considered

by averaging 12 × 12 blocks to ﬁnd widespread sta-

tistically signiﬁcant differences between groups over

the imaged area and across all retinal layers/layer-

complex. Details for the ONL are presented in Ta-

ble 3 as an example. A thorough analysis will be the

subject of a subsequent publication.

Imaging4OND 2022 - Special Session on New Developments in Imaging for Ocular and Neurodegenerative Disorders

254

Table 3: Features with statistically signiﬁcant differences between wild-type and transgenic mice groups for the outer nuclear

layer (ONL). The number of symbols identiﬁes the number of quadrants where the signiﬁcance was observed for each feature

shown. The green-coloured circles (○) represent a p-value ≤ 0.05, the orange-coloured squares () show p-values ≤ 0.01

and the red-coloured asterisks (*) represent a p-value ≤ 0.001.

Layer Feature

Age (months)

1 2 3 4

ONL

Autocorrelation **** **** *** ○*

Contrast **** **** *** *

Correlation ** ○ ○ ○*

Cluster Prominence **** **** *** *

Cluster Shade **** **** **** ○○

Dissimilarity **** **** **** **○

Energy **** **** **** *○

Entropy **** *** **

Homogeneity **** **** *** *

Maximum Probability **** **** *** *

Sum of Squares **** **** **

Sum Average **** *** ** ○*

Sum Variance **** **** **** **○

Sum Entropy **** **** *** *

Difference Variance **** **** *** *

Difference Entropy ○  

IMC1 **** **** **** ○

IMC2 ○  ○

INN **** *** *** ○*

IDN **** *** ○** ○*

4 DISCUSSION AND

CONCLUSIONS

Along the work developed over three years, we con-

tinuously monitor two mice groups, a WT control

group and the 3×Tg-AD, by frequent imaging their

retina by OCT to gather knowledge on the healthy

ageing, the ageing of transgenic mice and the differ-

ence between groups in particular how these compare

over time.

This is the ﬁrst time mice are consistently imaged

since their young age (one-month-old), demonstrating

an immature retina and shedding light on changes at

the early stages of life.

We have developed a neural network model for

retina segmentation based on the U-Net architecture

and using a new weighting scheme to adapt it to our

objectives. This weighting scheme is pixel-based and

calculated based on the pixels’ distance to the nearest

interface, allowing the prioritisation of the areas close

to the interfaces to produce fewer errors at interface

locations.

To train this model, we used 114 mice from the

two groups. A semi-automated strategy was em-

ployed to determine the ground truth of the selected

B-scans, as the amount of data available makes it dif-

ﬁcult, if not impossible, to produce manual segmenta-

tion for all B-scans. A previously detailed algorithm

(Ferreira et al., 2020) was used to create the initial

segmentation, with manual review and corrections,

wherever needed, from expert graders.

The training and test sets were separated by

mouse, guaranteeing the complete separation between

the two groups. Furthermore, only data from the ﬁrst

four time-points were considered during training, re-

sulting in 3000 B-scans being used for training. These

were augmented to increase the training set and in-

troduce variations in the layer location and shape to

reduce the possibility of over-ﬁtting.

The results from the test set showed that the model

successfully segmented over 90% of A-scans for the

large majority of the volumes in the test set. We

present the number per group, eye and age by taking

this value as a threshold to consider a volume cor-

rectly segmented.

While we do not see any bias towards any partic-

ular group or time point, only 50% of the left eyes for

mice three and sixteen months old were correctly seg-

mented. A thorough analysis revealed that these eyes

came only from three mice, implying that these errors

might be speciﬁc to these mice and not a general lim-

itation of the segmentation process.

Shedding Light on Early Central Nervous System Changes for Alzheimer’s Disease through the Retina: An Animal Study

255

The developed normative data for retinal thick-

ness, both layer-speciﬁc and total retina thickness, up

to the age of sixteen months old, is a considerable as-

set by demonstrating the thickness evolution of the

same animals and providing a comparative reference

to other studies.

Also, the consistent imaging and computing of

fundus images from the retina of WT and 3×Tg-AD

and the texture analysis of those allowed to detect

differences between groups since one-month-old and

raise novel scientiﬁc questions on their meaning.

In conclusion, these preliminary results from the

ongoing study demonstrate the massive differences

between the retinas of WT and 3×Tg-AD mice since

one-month-old, both from the thickness and texture

analysis viewpoints.

ACKNOWLEDGEMENTS

This study was supported by The Portuguese Foun-

dation for Science and Technology (FCT) through

PTDC/EMD-EMD/28039/2017, UIDB/04950/2020,

Pest-UID/NEU/04539/2019, and by FEDER-

COMPETE through POCI-01-0145-FEDER-028039.

The authors would like to acknowledge the contri-

bution of all team members in the study reported:

ao Martins, Paula I. Moreira, Ant

onio Francisco

Ambr

osio and Miguel Castelo-Branco.

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