Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: DeepMRSeg: A convolutional deep neural network for anatomy and
  abnormality segmentation on MR images   Segmentation has been a major task in neuroimaging. A large number of
automated methods have been developed for segmenting healthy and diseased brain
tissues. In recent years, deep learning techniques have attracted a lot of
attention as a result of their high accuracy in different segmentation
problems. We present a new deep learning based segmentation method, DeepMRSeg,
that can be applied in a generic way to a variety of segmentation tasks. The
proposed architecture combines recent advances in the field of biomedical image
segmentation and computer vision. We use a modified UNet architecture that
takes advantage of multiple convolution filter sizes to achieve multi-scale
feature extraction adaptive to the desired segmentation task. Importantly, our
method operates on minimally processed raw MRI scan. We validated our method on
a wide range of segmentation tasks, including white matter lesion segmentation,
segmentation of deep brain structures and hippocampus segmentation. We provide
code and pre-trained models to allow researchers apply our method on their own
datasets.

14
The categories of this paper are: math-ph
28
The categories of this paper are: alg-geom
24
The categories of this paper are: dg-ga
8
The categories of this paper are: hep-ex
27
The categories of this paper are: comp-gas
35
The categories of this paper are: mtrl-th
33
The categories of this paper are: plasm-ph
36
The categories of this paper are: cmp-lg
34
The categories of this paper are: chem-ph
4
The categories of this paper are: cond-mat
6
The categories of this paper are: astro-ph
31
The categories of this paper are: ao-sci
5
The categories of this paper are: gr-qc
30
The categories of this paper are: q-alg
7
The categories of this paper are: hep-th
26
The categories of this paper are: bayes-an