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22 Retinal Nerve Fiber Layer
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261
Technology: Several early techniques were
used to nd RNFL defect patches in a fundus
image without needing OCT imaging [30, 31].
However, these early methods failed to demarcate the RNFL defect boundary explicitly. Oh
etal. suggested a new method for localizing the
RNFL defect boundaries [32]. In their approach,
the RNFL defect edges were rst detected by
incorporating Canny’s operator, and then lines
were tted using the Hough transformation. The
false positives were reduced using decision
rules based on the average intensity, vertical
length, and angular placement among the
observed lines.
However, false positives are not entirely
eliminated with this method. After removing the
Table 22.1
1 Recurrent neural
2 Automated retinal nerve
3 Deep convolutional neural
Summary of all methods for retinal nerve ber layer defect (RNFLD) analysis in fundus images
Methodology Recurrent neural network (RNN)
network-based retinal
nerve ber layer defect
detection in early
glaucoma
ber layer defect detection
using fundus imaging in
glaucoma
network-based patch
classication for retinal
nerve ber layer defect
detection in early
glaucoma
Dataset Dataset from LV Prasad Eye Institute, Bhubaneswar,
Model parameter No. of hidden layers: 5
Performance measure Euclidean distance
Advantages Accomplishes accurate RNFLD detection for the
Methodology Random forest classier
Dataset Dataset from LV Prasad Eye Institute, Bhubaneswar,
Model parameter Input patch size: 11×11 pixels
Performance measure Sensitivity vs. false positives per image (FPI) and
Advantages It measures the angular width of the RNFLD region,
Methodology CNN and random sample consensus (RANSAC)-
Dataset Dataset from LV Prasad Eye Institute, Bhubaneswar,
Model parameter Batch size: 64
Performance measure Sensitivity vs. false positives per image (FPI) and
Advantages Learning of patch-based image features directly
retinal blood vessels, Muramatsu etal. [33] utilized Gabor ltering for augmented detection of
the RNFL defect. The mean and standard deviation of neighborhood intensities are used to
locate the candidate RNFL defect boundary pixels. The ANN (Articial Neural Network) classier is then used to reduce the false positives.
The primary drawback of this method is that it
denes the relationship between the RNFL
defect boundary and neighborhood intensities
by utilizing several empirically established
image processing-based factors.
Recent Machine and Deep Learning-Based
RNFL Defect Detection. Several methods for
detecting RNFL defects are proposed
(Table22.1). Some of these are mentioned below.
India
Input patch size: 11×11 pixels
newly developed dataset.
India
Cluster size: 0.4 times the maximum cluster size
No. of trees: 30
Max. depth of tree: 20
AUC
which can be used to track the progression of
glaucoma
based line tting algorithm
India
No. of epochs: 100
Learning rate: 0.0001
AUC
using a CNN, as opposed to the classiers learning
from manually created features
(continued)
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Table 22.1 (continued)
4 A large-scale database and
a CNN model for
attention- based glaucoma
detection
5 A novel adaptive weighted
loss design in adversarial
learning for retinal nerve
ber layer defect
segmentation
6 Retinal nerve ber layer
defect detection with
position guidance
7 Retinal nerve ber layer
defect detection using
machine learning on optic
disc photograph
8 Detecting retinal nerve
ber layer using gray level
co-occurrence matrix and
machine learning
approach
A. Rao and N. B. Puhan
Methodology AG-CNN
Dataset LAG and RIM -ONE
Model parameter Batch size: 8
Optimizer: Adam
Learning rate: 0.00001
Performance measure Accuracy, sensitivity, specicity, AUC and
F2-score
Advantages The deployed attention maps emphasize the most
crucial regions for glaucoma detection
Methodology Conditional adversarial shufe U-shaped network
(CASU-Net)
Dataset Dataset from Beijing Tongren Hospital, China
Model parameter Learning rate: 0.0001
Optimizer: Adam
Shufe module, p: 0.8
Threshold: 0.5
Performance measure AUC, F-score, MAP, M-IoU, sensitivity, and
specicity
Advantages It analyzes the overall geometric and pixel-level
discrepancies between the RNFLD prediction result
and ground truth
Methodology Position-Guided Network (PGN)
Dataset Dataset from the Department of Ophthalmology,
Peking Union Medical College Hospital, China
Model parameter Optimizer: Stochastic Gradient Descent
Momentum: 0.9
Weight decay: 0.0001
Learning rate: 0.02
No. of epochs: 20
Performance measure Accuracy, specicity, sensitivity, and F1-score
Advantages The suggested method can be applied to other
position-related diseases and is particularly useful
for detecting RNFLD since it can sense position in
both input data and the network
Methodology Gaussian SVM, kNN, and ensemble RUSBoosted
Trees
Dataset Dataset from King Chulalongkorn Memorial
Hospital, Thailand
Model parameter –
Performance measure Sensitivity, specicity, accuracy, and AUC
Advantages This technique did not require complex image
processing; therefore, it may be suited for image
capture and classication using a mobile phone
Methodology Random forest classier
Dataset Private dataset
Model parameter
Performance measure Accuracy, precision, and recall
Advantages The method obtained excellent results on the used
private dataset.
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Table 22.1 (continued)
9 Active transfer learning
network for retinal nerve
ber layer (RNFL) loss/
defect diagnosis from
digital fundus images
263
Methodology Active transfer learning-based framework with
VGG16
Dataset Dataset from All-India Institute of Medical Sciences
(AIIMS), New Delhi, India
Model parameter Dropout: 0.1
Activation function: ReLU
Optimizer: Adam
Learning rate: 0.000001
Batch size: 32
No. of epochs: 50
Performance measure Accuracy, AUC, sensitivity vs. false positive per
image
Advantages Existing methods typically considers high-gradient
regions with noisy information, which can distract
an RNFL loss diagnosis system. This problem is
resolved by the proposed method
22.5.1 Recurrent Neural Network
(RNN)
It utilizes fundus images using a patch featuredriven recurrent neural network [34]. The
essential steps in this method are (1) blood
vessel inpainting and contrast enhancement;
(2) finding the initial set of RNFL defect
boundary pixels by examining concentric 1-D
intensity profiles around the optic disc; and
(3) determining the true boundary by using the
recurrent neural networks (RNN) classifier.
The mean Euclidean distance between the
detected boundary pixels and the actual boundary is used to measure the accuracy of the
method. The proposed method achieved high
accuracy for the newly created fundus image
datasets [34].
22.5.2 Contrast-Limited Adaptive
Histogram Equalization
(CLAHE) [35]
The one-dimensional intensity proles derived
from several concentric circles around the optic
disc region are subjected to wavelet-based local
minima analysis to identify the possible RNFL
defect boundary pixels. In addition to Shannon
and Tsallis entropy and intensity-based features,
novel features like Cumulative Zero Count Local
Binary Pattern (CZC-LBP) and Directional
Differential Energy (DDE) have been developed.
For precise RNFL defect localization, the feature
vectors train the random forest classier. The
Random Sample Consensus (RANSAC) algorithm is used to line-t the detected boundary
pixels to calculate the RNFL defect’s angular
width. However, sometimes this method generates false-positive results in the presence of hemorrhages or dark pigmentation at the macula.
22.5.3 Deep Convolutional Neural
Network (CNN)
In this approach, after blood vessel inpainting,
contrast enhancement, and disc localization, a
region of interest is rst chosen around the optic
disc [36]. The rst step is to examine the onedimensional intensity proles on concentric circles surrounding the optic disc to obtain the
initial set of RNFL defect boundary pixels. By
creating a CNN that is trained on a large number
of image patches, the true border pixels are
retained. Unlike other methods, the proposed
architecture directly learns the important features
from many fundus image patches. Figure 22.6
shows the RNFL defect boundary detection outputs for multiple test fundus images.
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A. Rao and N. B. Puhan
Fig. 22.6 RNFL defect boundary detection results (Red: ground truth, Green: truly detected RNFL defect (TP), Blue:
falsely detected RNFLD (FP)) [36]
22.5.4 Attention-Based
Convolutional Neural Network
(AG-CNN) [37]
To build this dataset, the attention maps for 5824
of 11,760 fundus images were collected from ophthalmologists as part of a simulated eye- tracking
experiment. Then, an attention prediction subnet, a
pathological area localization subnet, and a glaucoma classication subnet were designed as part
of a new AG-CNN structure. Under a weakly
supervised training method, the attention prediction subnet predicted the attention maps to highlight the salient regions for glaucoma detection.
Finally, tests were run on the existing RIM-ONE
and the new LAG datasets. This technique effectively reduced high levels of redundancy in fundus
images for glaucoma identication, improving the
accuracy and reliability of glaucoma detection.
The attention maps employed in building the
AG-CNN were enhanced to highlight the most
important area for glaucoma detection.
22.5.5 Conditional Adversarial
Shue U-Shaped Network
(CASU-Net) [38]
It comprises a generator and a discriminator to
segment the RNFL defect region in the fundus
images. This technique combines an adversarial
loss and an adaptive weighted segmentation loss
to provide a mixed loss for the generator. The
CASU-Net increases the segmentation accuracy
at the pixel level and brings the geometry of the
target area closer to the ground truth. An adaptive
weighted (AW) segmentation loss is created for
the generator. The proposed network is focused
on the pixel-level and overall geometry differences between the RNFL defect prediction output and the ground truth.
22.5.6 Alternative Methods
Some alternative methods are described below.
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22.5.6.1 Position-Consistent Data
Pre-processing
andaPosition- Guided
Network [39]
It pre-processes input images using a constant
coordinate system to give the absolution positions of pixels in fundus images. A locationguided network, which considers the
physiological location and global dependencies
of RNFL in fundus images, is created for sector
classication. The suggested method senses positions in both the input data and the network; as a
result, it is particularly useful for detecting RNFL
defects and can be applied to other positionrelated disorders. Additionally, it deals with the
noise labels that the RNFL defect experiences
and which cause low generalization errors.
A machine learning classier on the color
information of the RNFL distinguishes between
RNFL defects (d-RNFL) and intact RNFLs
(i-RNFL) on the images of optic discs [40]. With
this technique, a semi-circle drawn on the disc
photos distinguishes the i-RNFL and
d-RNFL.The RGB intensities and various color
spaces of both proles are gathered. Finally, ve
classiers’ classication efcacies are compared
using vefold cross-validation. The accuracy of a
new feature was examined using a semiautomated technique that required an ophthalmologist to select a region and let the algorithm
determine the presence/absence of the
RNFL. This technique used simple image processing, making it potentially appropriate for
screening when utilizing a mobile phone to take
the image and identify it. However, the applicability of this technology may be constrained by
variations among different fundus cameras.
22.6 Random Forest andGray
Level Co-occurrence Matrix
(GLCM) [41]
The main steps for this method are: (1) dening
the region of interest by segmentation with
K-means clustering and segmenting the fundus
image into 12 sub-sectors; (2) forming the area of
feature extraction; (3) pre-processing by making
265
Fig. 22.7 Block diagram of the active transfer learningbased RNFL defect detection method [42]
the image grayscale; (4) feature extraction using
gray level co-occurrence matrix; and (5) classication using random forest classier. The authors
reported high-performance measures using this
technique on various fundus image datasets.
Sharma et al. have proposed an automated
method for the early diagnosis of RNFL loss
using fundus images (Fig.22.7) [42]. It aims to
nd RNFL defect patches using an active transfer
learning-based framework (with the VGG16 pretrained network as the backbone). In a limited
labelled training scenario, RNFL loss diagnosis
is accounted for by combining active and transfer
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A. Rao and N. B. Puhan
learning. The patch-based sampling method performs RNFL loss detection for the entire image
while using patches for the deep neural network.
The current methods frequently consider targeted
regions (high-gradient regions) but ultimately
consider noisy information, which confuses an
RNFL loss detection system. The suggested technique could resolve this issue.
22.7 Conclusion
Examining the retinal nerve ber layer in glaucoma is of utmost importance in diagnosing and
monitoring glaucoma progression. Recent developments in the imaging and quantication of the
retinal nerve ber layer using newer technologies
and articial intelligence enable automated
detection and quantication of the RNFL, which
will be a valuable asset if incorporated into existing fundus imaging systems. Such evolving paradigms will allow remote screening, diagnosis,
and monitoring of glaucoma, which may be part
of glaucoma telemedicine diagnostics.
Funding Hyderabad Eye Research Foundation,
Hyderabad, India (2023).
Disclosure None.
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Ultrasound Biomicroscopy
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MonaKhurana andVedvatiHemantAlbal
23
23.1 Introduction
The term “ultrasound biomicroscopy” (UBM)
was coined due to its similarity to optical biomicroscopy, that is, observing living tissues at a
microscopic resolution. It utilizes ultrasound frequencies in the 40–100MHz range to allow noninvasive invivo examinations of anterior segment
structures in the eye [1–5]. Images produced by
UBM have a resolution similar to those of a lowpower light microscope [3]. UBM can help in
imaging structures posterior to the iris pigment
epithelium, which does not allow light to pass
through but allows sound to pass through. This
makes UBM an investigation of choice to assess
the posterior chamber structures, including the
lens zonules, ciliary body, and anterior choroid,
compared to anterior segment optical coherence
tomography (AS-OCT) (Table 23.1). UBM has
multiple clinical applications, particularly in
evaluating corneal diseases, glaucoma, anterior
segment tumors, and trauma [4].
Table 23.1 Comparison of Ultrasound Biomicroscopy
and Anterior Segment-Optical Coherence Tomography
Anterior segment
Ultrasound
biomicroscopy
Features
Operating
system
Resolution
Tissue
penetration
Procedure Contact Non-contact
Acquisition
time
Ocular
tissue
Specic
advantage
(UBM)
Ultrasound
waves
Axial: 20–25μm
Lateral:
40–50μm
4–5mm 3–6mm
Long
(approximately
15minutes) [6]
Cornea: It gives
an approximate
measurement of
corneal thickness
Visualization of
the ciliary body,
posterior
chamber, lens,
and zonules
Imaging
non-pigmented
iris tumors,
ciliary body
neoplasm
optical coherence
tomography
(AS-OCT)
Near-infrared light
Axial: 5–10μm
Lateral: 15–25μm
Fast (up to
50,000A-scans/s)
[7]
Cornea: Excellent
visualization of all
layers of cornea and
reliable measure of
corneal thickness
Poor visualization
of the ciliary body
and posterior
chamber
Partial visualization
of the lens
Imaging ocular
surface squamous
neoplasia, cornea
M. Khurana (*) · V. H. Albal
Shri Nathmal Singhvi Jadhavbhai Glaucoma
Services, Sankara Nethralaya, Medical Research
Foundation, Chennai, India
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024
T. Das, P. Satgunam (eds.), Ophthalmic Diagnostics, https://doi.org/10.1007/978-981-97-0138-4_23
269
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23.2 History
The concept of acoustic imaging was introduced
by Marmor, who imaged sections of the retina and
iris in 1977 [8]. Pavlin etal. conducted invitro
studies to assess the use of high- frequency ultrasound (50–100MHz) for ophthalmic imaging and
developed the rst commercial instrument for
clinical use in 1989. UBM uses a 50MHz transducer to provide a good balance between resolution and depth of tissue penetration [1]. The
50MHz probe has a resolution of 37μm and a
4–5mm depth of tissue penetration [1].
23.3 Technology
Ultrasound consists of acoustic waves generated
by oscillating particles within a medium.
Ophthalmic ultrasound imaging utilizes higher
frequencies, 8–80MHz, compared to other diagnostic medical ultrasounds (2–6MHz). This provides increased resolution to evaluate ocular
structures which are more supercial [4]. As the
frequency of ultrasound increases, the acoustic
waves are more strongly attenuated, limiting the
depth of penetration. Higher frequencies
(7–10MHz) can be used to image smaller parts
of the body, such as the eye, where a penetration
of 4–5 cm is sufcient [1]. The higher the frequency, the higher the resolution.
The three tissue properties important in ultrasound imaging are attenuation, reectivity, and
the speed of sound. Attenuation is any interaction
that removes energy from the ultrasound beam. It
can be divided into absorption and reectivity
[1]. The absorbed energy is converted to thermal
energy. Reectivity is subdivided into specular
and scatter components.
The speed of ultrasound depends on the tissue
composition. Given their high water content, the
speed of sound in soft tissues is similar to that in
water. The highest speed of ultrasound is in the
sclera (1622 m/s), and comparatively lower
speeds are seen in the ciliary muscle (1554m/s)
and iris (1542m/s) [1, 9]. An ideal material with
uniform elastic properties will allow an ultrasound
wave to propagate undisturbed, and any material
with differing elastic properties will partly trans-
mit and partly reect the wave. Tissues have
structures that reect ultrasound, for example,
organ boundaries, blood vessels, and cellular and
intracellular structures. When the difference in
the tissue interface is larger than the ultrasound
wavelength, the reection is specular, like a partially reecting mirror. When the interface is
similar to or smaller than the ultrasound wavelength, the reected wave is called “scatter” [1].
23.3.1 Scanner Design
Commercially available machines use frequencies of 35 and 50MHz, whereas higher frequency
probes (80MHz) are used to image the cornea,
ocular surface, and anterior chamber [3], with a
2mm penetration depth. The transducer is excited
by a monocycle high voltage pulse of
40–100MHz. It is linearly moved over the imaging eld. Using a time-gain circuit, the transducer
receives a reected radiofrequency signal from
the tissues, further amplied in proportion to the
depth at which it originated. This compensates
for the attenuation of the ultrasound beam occurring in deeper tissues.
After processing the received signal to
enhance low-level signals, a section of the
detected signal corresponding to the focal zone
is digitized by a high-speed scan converter, and
the real-time image is displayed on a video
monitor [1, 2].
23.3.2 Transducers
High-resolution images can be obtained using
ultrasound with a higher frequency and shorter
focal length. However, the penetration is poor, and
deeper structures are not imaged clearly. Imaging
smaller tissues requires high-frequency ultrasound,
which in turn requires high-frequency transducers.
Thus, the transducer is an important component in
an ultrasound imaging system responsible for
image resolution, contrast, and sensitivity. The
transducer transforms electrical energy to acoustic
energy and back. It contains active piezoelectric
material that vibrates at the scanner materials’ resonant frequency [1]. The transducer is mounted on
cd
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the backing. There is an acoustic matching layer
and an electric matching circuit.
The range of depth over which the ultrasound
beam remains reasonably well focused is called
the depth of eld. The focusing characteristics of
the transducer are selected such that the tissue of
interest lies within the depth of eld [1]. The
quality of the image depends upon the transducer, the frequency of ultrasound, f-number
(ratio of the focal length to the transducer diameter), and length of the pulse. The axial resolution depends upon the speed of sound in the
tissue and the sound speed constant used in the
transducer. The lateral resolution is determined
by the distribution of ultrasound in the eld of
the transducer.
23.3.3 Scan Acquisition
The transducer has an oscillating probe without a
cover. A water bath is required to acquire scans.
A monocycle high voltage pulse excites the transducer. A 40–100 MHz ultrasound pulse is transmitted to the scanned tissue. The transducer is
moved along the image eld (4–5mm). It collects radiofrequency data at 512 equally spaced
lines (8μm apart). This signal is amplied nonlinearly in proportion to its depth of origin based
on the time-gain compensation. Signals from
deeper structures with more attenuation are
enhanced more than those from supercial struc-
tures. A scan is produced from the detected signal, converted to a digital format, and transferred
to a high-speed scan converter. The image is displayed on a video monitor at 5–10 frames per
second and stored. The transducer direction can
be assessed by looking at the image on the screen,
and the probe can be maneuvered to scan different tissue sections. Images are stored electronically on an attached computer with compatible
software.
23.4 Technique
UBM is performed under topical anesthesia with
the patient in a reclining or supine position. A
uid-immersion technique is used. Specially
designed scleral shells are available in various
sizes to hold the eyelids apart and serve as a reservoir for the coupling agent. Water (distilled
water) is effective as a coupling agent. The uid
level should be maintained throughout the scanning procedure. Air bubbles should be avoided as
air is a poor medium for sound transmission.
Methylcellulose (1%), an alternative coupling
agent, has low sound attenuation and greater viscosity, thus minimizing uid loss during the procedure [1, 4]. The UBM probe does not have a
membrane over the probe tip, as it would cause
signicant sound attenuation at the higher frequencies used. Thus, care must be taken to avoid
touching the cornea with the probe (Fig.23.1).
a
Fig. 23.1 (a) Scleral shell used during ultrasound biomi-
croscopy (UBM); (b) The scleral shell is positioned with
the patient in a reclining position. The scleral shell is
placed over the eye under topical anesthesia and is lled
with coupling uid; (c) UBM probe with the marker; (d)
b
UBM scan is acquired by dipping the oscillating tip of the
probe in the scleral shell lled with coupling uid. The
acquired scans are displayed and stored in the accompanying computer
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