Automated 3 d retinal layer segmentation of macular optical coherence tomography images with serous pigment epithelial detachments

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AUTOMATED 3-D RETINAL LAYER SEGMENTATION OF MACULAR OPTICAL
COHERENCE TOMOGRAPHY IMAGES WITH SEROUS PIGMENT EPITHELIAL
DETACHMENTS
By
A
PROJECT REPORT
Submitted to the Department of electronics &communication Engineering in the
FACULTY OF ENGINEERING & TECHNOLOGY
In partial fulfillment of the requirements for the award of the degree
Of
MASTER OF TECHNOLOGY
IN
ELECTRONICS &COMMUNICATION ENGINEERING
APRIL 2016

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CERTIFICATE
Certified that this project report titled “Automated 3-D Retinal Layer Segmentation of Macular
Optical Coherence Tomography Images With Serous Pigment Epithelial Detachments” is
the bonafide work of Mr. _____________Who carried out the research under my supervision
Certified further, that to the best of my knowledge the work reported herein does not form part of
any other project report or dissertation on the basis of which a degree or award was conferred on
an earlier occasion on this or any other candidate.
Signature of the Guide Signature of the H.O.D
Name Name

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DECLARATION
I hereby declare that the project work entitled “Automated 3-D Retinal Layer Segmentation of
Macular Optical Coherence Tomography Images With Serous Pigment Epithelial
Detachments” Submitted to BHARATHIDASAN UNIVERSITY in partial fulfillment of the
requirement for the award of the Degree of MASTER OF APPLIED ELECTRONICS is a record
of original work done by me the guidance of Prof.A.Vinayagam M.Sc., M.Phil., M.E., to the
best of my knowledge, the work reported here is not a part of any other thesis or work on the basis
of which a degree or award was conferred on an earlier occasion to me or any other candidate.
(Student Name)
(Reg.No)
Place:
Date:

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ACKNOWLEDGEMENT
I am extremely glad to present my project “Automated 3-D Retinal Layer Segmentation of
Macular Optical Coherence Tomography Images With Serous Pigment Epithelial
Detachments” which is a part of my curriculum of third semester Master of Science in Computer
science. I take this opportunity to express my sincere gratitude to those who helped me in bringing
out this project work.
I would like to express my Director,Dr. K. ANANDAN, M.A.(Eco.), M.Ed., M.Phil.,(Edn.),
PGDCA., CGT., M.A.(Psy.)of who had given me an opportunity to undertake this project.
I am highly indebted to Co-OrdinatorProf. Muniappan Department of Physics and thank from
my deep heart for her valuable comments I received through my project.
I wish to express my deep sense of gratitude to my guide
Prof. A.Vinayagam M.Sc., M.Phil., M.E., for her immense help and encouragement for
successful completion of this project.
I also express my sincere thanks to the all the staff members of Computer science for their kind
advice.
And last, but not the least, I express my deep gratitude to my parents and friends for their
encouragement and support throughout the project.

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ABSTRACT:
Automated retinal layer segmentation of optical coherence tomography (OCT) images has
been successful for normal eyes but becomes challenging for eyes with retinal diseases if the retinal
morphology experiences critical changes. We propose a method to automatically segment the
retinal layers in 3-D OCT data with serous retinal pigment epithelial detachments (PED), which is
a prominent feature of many chorioretinal disease processes. The proposed framework consists of
the following steps: fast denoising and B-scan alignment, multi-resolution graph search based
surface detection, PED region detection and surface correction above the PED region. The
proposed technique was evaluated on a dataset with OCTimages from 20 subjects diagnosed with
PED. The experimental results showed the following. 1) The overall mean unsigned border
positioning error for layer segmentation is , and is comparable to the mean inter-observer
variability ( ). 2) The true positive volume fraction (TPVF), false positive volume fraction (FPVF)
and positive predicative value (PPV) for PED volume segmentation are 87.1%, 0.37%, and 81.2%,
respectively. 3) The average running time is 220 s for OCT data of 512 64 480 voxels.

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INTRODUCTION:
Optical coherence tomography (OCT), a noninvasive, non-contact scan of the retina that
shows its cross-sectional profile, has been used clinically for assessment of a variety of ocular
diseases, such as glaucoma, diabetic macular edema (DME), and age-related macular degeneration
(AMD). Recently introduced spectral domain (SD) OCT produces highresolution real 3-D
volumetric scan of the retina, and most of the anatomical layers of the retina can be visualized.
Many methods have been proposed for automated retinal layer segmentation of SD-OCT images
of normal eyes, and have obtained satisfactory results. Fig. 1 shows a macular centered OCT B-
scan (axial view) of a normal eye and the 11 surfaces that define 10 retinal layers, segmented using
the Iowa Reference Algorithm. The surfaces are numbered 1–11 from top to bottom. The retinal
layers thus defined are nerve fiber layer (NFL), ganglion cell layer (GCL), inner plexiform layer
(IPL), inner nuclear layer (INL), outer plexiform layer (OPL), outer nuclear layer and inner
segment layer ( ), connecting cilia (CL), outer segment layer (OSL), Verhoeff's membrane (VM),
and retinal pigment epithelium (RPE).
Layer segmentation methods designed for normal retinas have also been successfully
applied to retinas with certain types of diseases, such as glaucoma, and multiple sclerosis or other
diseases at an early stage, when no dramatic change in the layer structure happens. However, they
usually experience difficulty when additional structures exist, such as intraretinal cysts, subretinal,
or sub-RPE fluid in DME and wet AMD. In these cases, layer segmentation becomes challenging
due to the following two reasons. First, the morphological features of each layer may vary greatly,
and some constraints such as layer smoothness and thickness may not apply as in the normal case.
Secondly, the degradation of image quality caused by abnormalities may affect the segmentation
performance. Therefore, new methods that can segment retinas with abnormalities are needed for
quantitative analysis of these diseases.
The significance of layer segmentation in pathological study and clinical practice lies in
the following two aspects. First, with the segmentation information, the morphological and optical
features of each individual layer and their difference from normal ones can be analyzed, which can
improve the understanding of the disease progression and also can facilitate diagnosis. Secondly,
layer segmentation can localize the abnormal regions and serve as a preprocessing step for
automated detection and analysis of the abnormalities.

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In this paper, we focus on segmentation for retinas with serous pigment epithelium
detachments (PEDs), which is associated with sub-RPE fluid and RPE deformation. We report a
fully automated, unsupervised 3-D layer segmentation method for macular-centered OCT images
with serous PEDs. In this work, layer segmentation and abnormal region segmentation are
effectively integrated, where the position of layers and regions serve as constraints for each other.
PED is a prominent feature of many chorioretinal disease processes, including AMD, polypoidal
choroidal vasculopathy, central serous chorioretinopathy, and uveitis. PEDs can be classified as
serous, fibrovascular, or drusenoid. Study shows that patients diagnosed with serous PED
associated with AMD frequently have co-existing choroidal neovascularization (CNV), or have a
higher risk of developing CNV, which can eventually cause severe visual acuity loss. PED is
routinely diagnosed by 2-D imaging techniques such as fluorescein angiography (FA) and
indocyanine green angiography (ICGV). More recently, SD-OCToffers a means to show the cross-
sectional morphologic characteristics of PED and to provide more detailed anatomic assessment.
In OCTimages, the RPE appears as a bright layer, and serous PED appears as a localized, relatively
pronounced dome-shaped elevation of the RPE layer, as shown
There were several reported methods related to segmentation of OCT images with PEDs
or other abnormalities. Penha et al. utilized the software on the commercially available Cirrus SD-
OCT to detect the RPE and a method proposed by Gregori et al. To create a virtual RPE floor free
of any deformations. The combination of these algorithms permitted the detection of PEDs. The
same algorithm was also used to study drusen associated with AMD ding et al. Detected the top
and bottom surfaces of the retina as constraints for subretinal and sub-RPE fluid detection. Chen
et al. segmented the fluid-associated abnormalities associated with AMD using a combined graph-
search–graph-cut (GS-GC) method. The abnormal region was detected together with two auxiliary
surfaces. Dufour et al. Detected six surfaces using graph-search based method with soft constraints
in OCT images with drusen. Quellec et al. segmented 11 surfaces in OCT images with fluid-
associated abnormalities. However, all these works focused on region segmentation only. In only
two or three surfaces were detected and served as constraints for the region segmentation purpose.
In and more surfaces were detected and their position information was utilized to indicate or detect
retinal abnormalities. For all works reported in no evaluation of layer segmentation accuracy was
given.

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In comparison with the existing methods, the proposed method achieves the following goals.
• The retinal OCT image with PEDs is segmented into all discernible layers. •
Both layer segmentation and abnormal region segmentation are performed and high accuracy is
achieved. •
The method is designed for retinas with serous PEDs, but it also maintains good performance for
normal retinas.

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CONCLUSION:
We proposed an unsupervised method for automated segmentation of retinal layers on SD-
OCT scans of eyes with serous PED. After denoising using fast bilateral filtering, the B-scans are
aligned using the upper boundary of the retina. This alignment improves the smoothness of the
surfaces to be detected and enhances the accuracy of the segmentation. Then the surfaces defining
the boundaries between consecutive layers are detected based on multi-resolution single surface
graph search. Surfaces 1–6, and a surface combined by surfaces 7 and 10 are detected on the
denoised and aligned data. Then surfaces 11 and 12 corresponding to the elevated RPE and the
estimated normal RPE floor are also detected, whose difference is used to find the PED footprints.
Surface 11 is used as the reference surface for flattening of the retina. Then surface 7 is corrected
based on its smoothness on the flattened image. Surfaces 8–10 are also detected on the flattened
image with necessary corrections.
For the tested PED dataset, the overall layer segmentation errors are statistically
indistinguishable from the inter-observer variability, and statistically significantly smaller than
errors obtained from employing the general Iowa Reference Algorithm . Though the proposed
algorithm is designed for retinas with serous PED's, it also works well for normal retinas. For the
tested normal dataset, the overall layer segmentation errors are statistically smaller than the inter-
observer difference, and statistically indistinguishable from errors obtained from employing the
Iowa Reference Algorithm . Although the method is not the most efficient for normal retina
segmentation, it represents a major advancement of the field allowing segmentation of the retinal
layers in both normal and diseased retinal images, thus bypassing a need for disease-specific
diagnosis prior to retinal analysis
Simultaneous detection and segmentation of the PED volume is also achieved. The PED
volume segmentation is of high true positive ratio and low false positive ratio, which is statistically
comparable to the results obtained by the GS-GC method . The proposed algorithm is 3-D, but
some of the calculations, including denoising, gradient calculation for cost function and smoothing
of the detected surfaces, are constrained in 2-D B-scans. This allows big difference in adjacent B-
scans caused by low resolution in the direction of the available data. For OCT data with higher
resolution in the direction, their 3-D counterparts should be used to fully utilize the contextual
information. The proposed algorithm can be extended to other pathological cases where RPE
deformation occurs. It provides a means to remove the sub-RPE fluid region and to approximately

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restore the original shape of the retinal layers. This method can be extended to other pathological
cases where intraretinal or subretinal fluids are present. In the future, we will consider detection
of these regions and using the information to improve the layer segmentation results.
In summary, as an accurate and efficient replacement of manual segmentation, the
proposed algorithm can be utilized for quantitative analysis of features of individual retinal layers
for both eyes with serous PED's and normal eyes. The algorithm also detects the PED volume,
providing its size, shape, and position information. With the current efficiency, the reported work
can be used in offline clinical or pathology studies. However, with further optimization in
implementation, additional speed-up will be accomplished and the reported approach will become
suitable for clinical practice.

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Automated 3 d retinal layer segmentation of macular optical coherence tomography images with serous pigment epithelial detachments

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Automated 3 d retinal layer segmentation of macular optical coherence tomography images with serous pigment epithelial detachments