Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multi-stream 3D FCN with Multi-scale Deep Supervision for Multi-modality
  Isointense Infant Brain MR Image Segmentation   We present a method to address the challenging problem of segmentation of
multi-modality isointense infant brain MR images into white matter (WM), gray
matter (GM), and cerebrospinal fluid (CSF). Our method is based on
context-guided, multi-stream fully convolutional networks (FCN), which after
training, can directly map a whole volumetric data to its volume-wise labels.
In order to alleviate the poten-tial gradient vanishing problem during
training, we designed multi-scale deep supervision. Furthermore, context
infor-mation was used to further improve the performance of our method.
Validated on the test data of the MICCAI 2017 Grand Challenge on 6-month infant
brain MRI segmentation (iSeg-2017), our method achieved an average Dice Overlap
Coefficient of 95.4%, 91.6% and 89.6% for CSF, GM and WM, respectively.

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The categories of this paper are: patt-sol
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The categories of this paper are: dg-ga
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The categories of this paper are: nucl-th
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The categories of this paper are: bayes-an
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The categories of this paper are: atom-ph
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The categories of this paper are: nucl-ex
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The categories of this paper are: acc-phys
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The categories of this paper are: math
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The categories of this paper are: math-ph
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The categories of this paper are: gr-qc
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The categories of this paper are: nlin
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The categories of this paper are: ao-sci
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The categories of this paper are: q-fin
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The categories of this paper are: hep-th
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The categories of this paper are: cond-mat
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The categories of this paper are: alg-geom