Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: FD-FCN: 3D Fully Dense and Fully Convolutional Network for Semantic
  Segmentation of Brain Anatomy   In this paper, a 3D patch-based fully dense and fully convolutional network
(FD-FCN) is proposed for fast and accurate segmentation of subcortical
structures in T1-weighted magnetic resonance images. Developed from the seminal
FCN with an end-to-end learning-based approach and constructed by newly
designed dense blocks including a dense fully-connected layer, the proposed
FD-FCN is different from other FCN-based methods and leads to an outperformance
in the perspective of both efficiency and accuracy. Compared with the U-shaped
architecture, FD-FCN discards the upsampling path for model fitness. To
alleviate the problem of parameter explosion, the inputs of dense blocks are no
longer directly passed to subsequent layers. This architecture of FD-FCN brings
a great reduction on both memory and time consumption in training process.
Although FD-FCN is slimmed down, in model competence it gains better capability
of dense inference than other conventional networks. This benefits from the
construction of network architecture and the incorporation of redesigned dense
blocks. The multi-scale FD-FCN models both local and global context by
embedding intermediate-layer outputs in the final prediction, which encourages
consistency between features extracted at different scales and embeds
fine-grained information directly in the segmentation process. In addition,
dense blocks are rebuilt to enlarge the receptive fields without significantly
increasing parameters, and spectral coordinates are exploited for spatial
context of the original input patch. The experiments were performed over the
IBSR dataset, and FD-FCN produced an accurate segmentation result of overall
Dice overlap value of 89.81% for 11 brain structures in 53 seconds, with at
least 3.66% absolute improvement of dice accuracy than state-of-the-art 3D
FCN-based methods.

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The categories of this paper are: physics
31
The categories of this paper are: ao-sci
17
The categories of this paper are: q-fin
35
The categories of this paper are: mtrl-th
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The categories of this paper are: dg-ga
6
The categories of this paper are: astro-ph
36
The categories of this paper are: cmp-lg
27
The categories of this paper are: comp-gas
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The categories of this paper are: alg-geom
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The categories of this paper are: math-ph
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The categories of this paper are: stat
20
The categories of this paper are: acc-phys
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The categories of this paper are: quant-ph
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The categories of this paper are: hep-ph
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The categories of this paper are: nucl-th
32
The categories of this paper are: atom-ph