Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Cystoid macular edema segmentation of Optical Coherence Tomography
  images using fully convolutional neural networks and fully connected CRFs   In this paper we present a new method for cystoid macular edema (CME)
segmentation in retinal Optical Coherence Tomography (OCT) images, using a
fully convolutional neural network (FCN) and a fully connected conditional
random fields (dense CRFs). As a first step, the framework trains the FCN model
to extract features from retinal layers in OCT images, which exhibit CME, and
then segments CME regions using the trained model. Thereafter, dense CRFs are
used to refine the segmentation according to the edema appearance. We have
trained and tested the framework with OCT images from 10 patients with diabetic
macular edema (DME). Our experimental results show that fluid and concrete
macular edema areas were segmented with good adherence to boundaries. A
segmentation accuracy of $0.61\pm 0.21$ (Dice coefficient) was achieved, with
respect to the ground truth, which compares favourably with the previous
state-of-the-art that used a kernel regression based method ($0.51\pm 0.34$).
Our approach is versatile and we believe it can be easily adapted to detect
other macular defects.

5
The categories of this paper are: gr-qc
4
The categories of this paper are: cond-mat
34
The categories of this paper are: chem-ph
35
The categories of this paper are: mtrl-th
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The categories of this paper are: astro-ph
36
The categories of this paper are: cmp-lg
15
The categories of this paper are: nucl-ex
31
The categories of this paper are: ao-sci
22
The categories of this paper are: chao-dyn
27
The categories of this paper are: comp-gas
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The categories of this paper are: adap-org
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The categories of this paper are: q-bio
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The categories of this paper are: quant-ph
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The categories of this paper are: patt-sol
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The categories of this paper are: nlin
37
The categories of this paper are: supr-con