Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Cascaded Residual UNET for Fully Automated Segmentation of Prostate
  and Peripheral Zone in T2-weighted 3D Fast Spin Echo Images   Multi-parametric MR images have been shown to be effective in the
non-invasive diagnosis of prostate cancer. Automated segmentation of the
prostate eliminates the need for manual annotation by a radiologist which is
time consuming. This improves efficiency in the extraction of imaging features
for the characterization of prostate tissues. In this work, we propose a fully
automated cascaded deep learning architecture with residual blocks, Cascaded
MRes-UNET, for segmentation of the prostate gland and the peripheral zone in
one pass through the network. The network yields high Dice scores
($0.91\pm.02$), precision ($0.91\pm.04$), and recall scores ($0.92\pm.03$) in
prostate segmentation compared to manual annotations by an experienced
radiologist. The average difference in total prostate volume estimation is less
than 5%.

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The categories of this paper are: cmp-lg
32
The categories of this paper are: atom-ph
10
The categories of this paper are: q-bio
0
The categories of this paper are: hep-ph
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The categories of this paper are: ao-sci
3
The categories of this paper are: physics
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The categories of this paper are: comp-gas
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The categories of this paper are: math-ph
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The categories of this paper are: q-fin
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The categories of this paper are: hep-ex
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The categories of this paper are: acc-phys
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The categories of this paper are: nucl-th
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The categories of this paper are: quant-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: nlin
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The categories of this paper are: stat