Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Segmentation of MRI head anatomy using deep volumetric networks and
  multiple spatial priors   Purpose: Conventional automated segmentation of the head anatomy in MRI
distinguishes different brain and non-brain tissues based on image intensities
and prior tissue probability maps (TPM). This works well for normal head
anatomies, but fails in the presence of unexpected lesions. Deep convolutional
neural networks leverage instead spatial patterns and can learn to segment
lesions, but often ignore prior probabilities. Approach: We add three sources
of prior information to a three-dimensional convolutional network, namely,
spatial priors with a TPM, morphological priors with conditional random fields,
and spatial context with a wider field-of-view at lower resolution. We train
and test these networks on 3D images of 43 stroke patients and 4 healthy
individuals which have been manually segmented. Results: We demonstrate the
benefits of each sources of prior information, and we show that the new
architecture, which we call Multiprior network, improves the performance of
existing segmentation software, such as SPM, FSL, and DeepMedic for abnormal
anatomies. The relevance of the different priors was compared and the TPM was
found to be most beneficial. The benefit of adding a TPM is generic in that it
can boost the performance of established segmentation networks such as the
DeepMedic and a UNet. We also provide an out-of-sample validation and clinical
application of the approach on an additional 47 patients with disorders of
consciousness. We make the code and trained networks freely available.
Conclusions: Biomedical images follow imaging protocols that can be leveraged
as prior information into deep convolutional neural networks to improve
performance. The network segmentations match human manual corrections performed
in 3D, and are comparable in performance to human segmentations obtained from
scratch in 2D for abnormal brain anatomies.

22
The categories of this paper are: chao-dyn
37
The categories of this paper are: supr-con
34
The categories of this paper are: chem-ph
23
The categories of this paper are: solv-int
27
The categories of this paper are: comp-gas
14
The categories of this paper are: math-ph
8
The categories of this paper are: hep-ex
5
The categories of this paper are: gr-qc
24
The categories of this paper are: dg-ga
32
The categories of this paper are: atom-ph
11
The categories of this paper are: hep-lat
13
The categories of this paper are: quant-ph
9
The categories of this paper are: nlin
6
The categories of this paper are: astro-ph
3
The categories of this paper are: physics
12
The categories of this paper are: nucl-th