Accurate Tissue Interface Segmentation via
Adversarial Pre-Segmentation of Anterior
Segment OCT Images
Posted on 2019-09-20 - 17:03
Optical Coherence Tomography (OCT) is an imaging modality that has been widely
adopted for visualizing corneal, retinal and limbal tissue structure with micron resolution. It can
be used to diagnose pathological conditions of the eye, and for developing pre-operative surgical
plans. In contrast to the posterior retina, imaging the anterior tissue structures, such as the limbus
and cornea, results in B-scans that exhibit increased speckle noise patterns and imaging artifacts.
These artifacts, such as shadowing and specularity, pose a challenge during the analysis of the
acquired volumes as they substantially obfuscate the location of tissue interfaces. To deal with
the artifacts and speckle noise patterns and accurately segment the shallowest tissue interface, we
propose a cascaded neural network framework, which comprises of a conditional Generative
Adversarial Network (cGAN) and a Tissue Interface Segmentation Network (TISN). The cGAN
pre-segments OCT B-scans by removing undesired specular artifacts and speckle noise patterns
just above the shallowest tissue interface, and the TISN combines the original OCT image with the
pre-segmentation to segment the shallowest interface. We show the applicability of the cascaded
framework to corneal datasets, demonstrate that it precisely segments the shallowest corneal
interface, and also show its generalization capacity to limbal datasets. We also propose a hybrid
framework, wherein the cGAN pre-segmentation is passed to a traditional image analysis-based
segmentation algorithm, and describe the improved segmentation performance. To the best of
our knowledge, this is the first approach to remove severe specular artifacts and speckle noise
patterns (prior to the shallowest interface) that affects the interpretation of anterior segment OCT
datasets, thereby resulting in the accurate segmentation of the shallowest tissue interface. To the
best of our knowledge, this is the first work to show the potential of incorporating a cGAN into
larger deep learning frameworks for improved corneal and limbal OCT image segmentation. Our
cGAN design directly improves the visualization of corneal and limbal OCT images from OCT
scanners, and improves the performance of current OCT segmentation algorithms.
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Ouyang, Jiahong; Mathai, Tejas Sudharshan; Lathrop, Kira; Galeotti, John (2019). Accurate Tissue Interface Segmentation via
Adversarial Pre-Segmentation of Anterior
Segment OCT Images. Optica Publishing Group. Collection. https://doi.org/10.6084/m9.figshare.c.4495424.v1
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AUTHORS (4)
JO
Jiahong Ouyang
TM
Tejas Sudharshan Mathai
KL
Kira Lathrop
JG
John Galeotti