Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Recurrent Fully Convolutional Neural Networks for Multi-slice MRI
  Cardiac Segmentation   In cardiac magnetic resonance imaging, fully-automatic segmentation of the
heart enables precise structural and functional measurements to be taken, e.g.
from short-axis MR images of the left-ventricle. In this work we propose a
recurrent fully-convolutional network (RFCN) that learns image representations
from the full stack of 2D slices and has the ability to leverage inter-slice
spatial dependences through internal memory units. RFCN combines anatomical
detection and segmentation into a single architecture that is trained
end-to-end thus significantly reducing computational time, simplifying the
segmentation pipeline, and potentially enabling real-time applications. We
report on an investigation of RFCN using two datasets, including the publicly
available MICCAI 2009 Challenge dataset. Comparisons have been carried out
between fully convolutional networks and deep restricted Boltzmann machines,
including a recurrent version that leverages inter-slice spatial correlation.
Our studies suggest that RFCN produces state-of-the-art results and can
substantially improve the delineation of contours near the apex of the heart.

37
The categories of this paper are: supr-con
26
The categories of this paper are: bayes-an
36
The categories of this paper are: cmp-lg
33
The categories of this paper are: plasm-ph
0
The categories of this paper are: hep-ph
3
The categories of this paper are: physics
29
The categories of this paper are: funct-an
7
The categories of this paper are: hep-th
11
The categories of this paper are: hep-lat
20
The categories of this paper are: acc-phys
32
The categories of this paper are: atom-ph
22
The categories of this paper are: chao-dyn
4
The categories of this paper are: cond-mat
30
The categories of this paper are: q-alg
12
The categories of this paper are: nucl-th
35
The categories of this paper are: mtrl-th