Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Recurrent Level Set for Segmenting Brain Tumors   Variational Level Set (VLS) has been a widely used method in medical
segmentation. However, segmentation accuracy in the VLS method dramatically
decreases when dealing with intervening factors such as lighting, shadows,
colors, etc. Additionally, results are quite sensitive to initial settings and
are highly dependent on the number of iterations. In order to address these
limitations, the proposed method incorporates VLS into deep learning by
defining a novel end-to-end trainable model called as Deep Recurrent Level Set
(DRLS). The proposed DRLS consists of three layers, i.e, Convolutional layers,
Deconvolutional layers with skip connections and LevelSet layers. Brain tumor
segmentation is taken as an instant to illustrate the performance of the
proposed DRLS. Convolutional layer learns visual representation of brain tumor
at different scales. Since brain tumors occupy a small portion of the image,
deconvolutional layers are designed with skip connections to obtain a high
quality feature map. Level-Set Layer drives the contour towards the brain
tumor. In each step, the Convolutional Layer is fed with the LevelSet map to
obtain a brain tumor feature map. This in turn serves as input for the LevelSet
layer in the next step. The experimental results have been obtained on
BRATS2013, BRATS2015 and BRATS2017 datasets. The proposed DRLS model improves
both computational time and segmentation accuracy when compared to the the
classic VLS-based method. Additionally, a fully end-to-end system DRLS achieves
state-of-the-art segmentation on brain tumors.

21
The categories of this paper are: adap-org
15
The categories of this paper are: nucl-ex
29
The categories of this paper are: funct-an
12
The categories of this paper are: nucl-th
3
The categories of this paper are: physics
13
The categories of this paper are: quant-ph
4
The categories of this paper are: cond-mat
27
The categories of this paper are: comp-gas
33
The categories of this paper are: plasm-ph
6
The categories of this paper are: astro-ph
0
The categories of this paper are: hep-ph
32
The categories of this paper are: atom-ph
16
The categories of this paper are: stat
28
The categories of this paper are: alg-geom
11
The categories of this paper are: hep-lat
8
The categories of this paper are: hep-ex