Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: ARPM-net: A novel CNN-based adversarial method with Markov Random Field
  enhancement for prostate and organs at risk segmentation in pelvic CT images   Purpose: The research is to develop a novel CNN-based adversarial deep
learning method to improve and expedite the multi-organ semantic segmentation
of CT images, and to generate accurate contours on pelvic CT images. Methods:
Planning CT and structure datasets for 120 patients with intact prostate cancer
were retrospectively selected and divided for 10-fold cross-validation. The
proposed adversarial multi-residual multi-scale pooling Markov Random Field
(MRF) enhanced network (ARPM-net) implements an adversarial training scheme. A
segmentation network and a discriminator network were trained jointly, and only
the segmentation network was used for prediction. The segmentation network
integrates a newly designed MRF block into a variation of multi-residual U-net.
The discriminator takes the product of the original CT and the
prediction/ground-truth as input and classifies the input into fake/real. The
segmentation network and discriminator network can be trained jointly as a
whole, or the discriminator can be used for fine-tuning after the segmentation
network is coarsely trained. Multi-scale pooling layers were introduced to
preserve spatial resolution during pooling using less memory compared to atrous
convolution layers. An adaptive loss function was proposed to enhance the
training on small or low contrast organs. The accuracy of modeled contours was
measured with the Dice similarity coefficient (DSC), Average Hausdorff Distance
(AHD), Average Surface Hausdorff Distance (ASHD), and relative Volume
Difference (VD) using clinical contours as references to the ground-truth. The
proposed ARPM-net method was compared to several stateof-the-art deep learning
methods.

32
The categories of this paper are: atom-ph
8
The categories of this paper are: hep-ex
12
The categories of this paper are: nucl-th
24
The categories of this paper are: dg-ga
3
The categories of this paper are: physics
18
The categories of this paper are: econ
34
The categories of this paper are: chem-ph
14
The categories of this paper are: math-ph
11
The categories of this paper are: hep-lat
2
The categories of this paper are: math
36
The categories of this paper are: cmp-lg
0
The categories of this paper are: hep-ph
35
The categories of this paper are: mtrl-th
9
The categories of this paper are: nlin
7
The categories of this paper are: hep-th
27
The categories of this paper are: comp-gas