Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A deep learning classification scheme based on augmented-enhanced
  features to segment organs at risk on the optic region in brain cancer
  patients   Radiation therapy has emerged as one of the preferred techniques to treat
brain cancer patients. During treatment, a very high dose of radiation is
delivered to a very narrow area. Prescribed radiation therapy for brain cancer
requires precisely defining the target treatment area, as well as delineating
vital brain structures which must be spared from radiotoxicity. Nevertheless,
delineation task is usually still manually performed, which is inefficient and
operator-dependent. Several attempts of automatizing this process have
reported. however, marginal results when analyzing organs in the optic region.
In this work we present a deep learning classification scheme based on
augmented-enhanced features to automatically segment organs at risk (OARs) in
the optic region -optic nerves, optic chiasm, pituitary gland and pituitary
stalk-. Fifteen MR images with various types of brain tumors were
retrospectively collected to undergo manual and automatic segmentation. Mean
Dice Similarity coefficients around 0.80 were reported. Incorporation of
proposed features yielded to improvements on the segmentation. Compared with
support vector machines, our method achieved better performance with less
variation on the results, as well as a considerably reduction on the
classification time. Performance of the proposed approach was also evaluated
with respect to manual contours. In this case, results obtained from the
automatic contours mostly lie on the variability of the observers, showing no
significant differences with respect to them. These results suggest therefore
that the proposed system is more accurate than other presented approaches, up
to date, to segment these structures. The speed, reproducibility, and
robustness of the process make the proposed deep learning-based classification
system a valuable tool for assisting in the delineation task of small OARs in
brain cancer.

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The categories of this paper are: gr-qc
30
The categories of this paper are: q-alg
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The categories of this paper are: ao-sci
27
The categories of this paper are: comp-gas
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The categories of this paper are: nucl-ex
22
The categories of this paper are: chao-dyn
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The categories of this paper are: math
29
The categories of this paper are: funct-an
16
The categories of this paper are: stat
4
The categories of this paper are: cond-mat
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The categories of this paper are: solv-int
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The categories of this paper are: math-ph
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The categories of this paper are: quant-ph
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The categories of this paper are: nucl-th
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The categories of this paper are: econ
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The categories of this paper are: patt-sol