Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Semantic Segmentation of Human Thigh Quadriceps Muscle in Magnetic
  Resonance Images   This paper presents an end-to-end solution for MRI thigh quadriceps
segmentation. This is the first attempt that deep learning methods are used for
the MRI thigh segmentation task. We use the state-of-the-art Fully
Convolutional Networks with transfer learning approach for the semantic
segmentation of regions of interest in MRI thigh scans. To further improve the
performance of the segmentation, we propose a post-processing technique using
basic image processing methods. With our proposed method, we have established a
new benchmark for MRI thigh quadriceps segmentation with mean Jaccard
Similarity Index of 0.9502 and processing time of 0.117 second per image.

37
The categories of this paper are: supr-con
29
The categories of this paper are: funct-an
18
The categories of this paper are: econ
22
The categories of this paper are: chao-dyn
27
The categories of this paper are: comp-gas
30
The categories of this paper are: q-alg
19
The categories of this paper are: eess
24
The categories of this paper are: dg-ga
23
The categories of this paper are: solv-int
6
The categories of this paper are: astro-ph
15
The categories of this paper are: nucl-ex
8
The categories of this paper are: hep-ex
26
The categories of this paper are: bayes-an
0
The categories of this paper are: hep-ph
2
The categories of this paper are: math
21
The categories of this paper are: adap-org