Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Accurate Tissue Interface Segmentation via Adversarial Pre-Segmentation
  of Anterior Segment OCT Images   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.

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The categories of this paper are: cond-mat
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The categories of this paper are: quant-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: cmp-lg
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The categories of this paper are: nucl-th
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The categories of this paper are: hep-lat
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The categories of this paper are: ao-sci
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The categories of this paper are: gr-qc
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The categories of this paper are: q-fin
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The categories of this paper are: supr-con
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The categories of this paper are: dg-ga
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The categories of this paper are: q-bio
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The categories of this paper are: econ
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The categories of this paper are: patt-sol
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The categories of this paper are: bayes-an
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The categories of this paper are: physics