Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Learning in Medical Ultrasound Image Segmentation: a Review   Applying machine learning technologies, especially deep learning, into
medical image segmentation is being widely studied because of its
state-of-the-art performance and results. It can be a key step to provide a
reliable basis for clinical diagnosis, such as 3D reconstruction of human
tissues, image-guided interventions, image analyzing and visualization. In this
review article, deep-learning-based methods for ultrasound image segmentation
are categorized into six main groups according to their architectures and
training at first. Secondly, for each group, several current representative
algorithms are selected, introduced, analyzed and summarized in detail. In
addition, common evaluation methods for image segmentation and ultrasound image
segmentation datasets are summarized. Further, the performance of the current
methods and their evaluations are reviewed. In the end, the challenges and
potential research directions for medical ultrasound image segmentation are
discussed.

31
The categories of this paper are: ao-sci
32
The categories of this paper are: atom-ph
37
The categories of this paper are: supr-con
36
The categories of this paper are: cmp-lg
8
The categories of this paper are: hep-ex
34
The categories of this paper are: chem-ph
4
The categories of this paper are: cond-mat
20
The categories of this paper are: acc-phys
15
The categories of this paper are: nucl-ex
11
The categories of this paper are: hep-lat
28
The categories of this paper are: alg-geom
6
The categories of this paper are: astro-ph
30
The categories of this paper are: q-alg
14
The categories of this paper are: math-ph
9
The categories of this paper are: nlin
33
The categories of this paper are: plasm-ph