Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Ultrasound segmentation using U-Net: learning from simulated data and
  testing on real data   Segmentation of ultrasound images is an essential task in both diagnosis and
image-guided interventions given the ease-of-use and low cost of this imaging
modality. As manual segmentation is tedious and time consuming, a growing body
of research has focused on the development of automatic segmentation
algorithms. Deep learning algorithms have shown remarkable achievements in this
regard; however, they need large training datasets. Unfortunately, preparing
large labeled datasets in ultrasound images is prohibitively difficult.
Therefore, in this study, we propose the use of simulated ultrasound (US)
images for training the U-Net deep learning segmentation architecture and test
on tissue-mimicking phantom data collected by an ultrasound machine. We
demonstrate that the trained architecture on the simulated data is
transferrable to real data, and therefore, simulated data can be considered as
an alternative training dataset when real datasets are not available. The
second contribution of this paper is that we train our U- Net network on
envelope and B-mode images of the simulated dataset, and test the trained
network on real envelope and B- mode images of phantom, respectively. We show
that test results are superior for the envelope data compared to B-mode image.

23
The categories of this paper are: solv-int
28
The categories of this paper are: alg-geom
14
The categories of this paper are: math-ph
8
The categories of this paper are: hep-ex
24
The categories of this paper are: dg-ga
2
The categories of this paper are: math
15
The categories of this paper are: nucl-ex
0
The categories of this paper are: hep-ph
12
The categories of this paper are: nucl-th
4
The categories of this paper are: cond-mat
9
The categories of this paper are: nlin
5
The categories of this paper are: gr-qc
7
The categories of this paper are: hep-th
20
The categories of this paper are: acc-phys
13
The categories of this paper are: quant-ph
33
The categories of this paper are: plasm-ph