Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Learning in Multi-organ Segmentation   This paper presents a review of deep learning (DL) in multi-organ
segmentation. We summarized the latest DL-based methods for medical image
segmentation and applications. These methods were classified into six
categories according to their network design. For each category, we listed the
surveyed works, highlighted important contributions and identified specific
challenges. Following the detailed review of each category, we briefly
discussed its achievements, shortcomings and future potentials. We provided a
comprehensive comparison among DL-based methods for thoracic and head & neck
multiorgan segmentation using benchmark datasets, including the 2017 AAPM
Thoracic Auto-segmentation Challenge datasets and 2015 MICCAI Head Neck
Auto-Segmentation Challenge datasets.

21
The categories of this paper are: adap-org
11
The categories of this paper are: hep-lat
29
The categories of this paper are: funct-an
25
The categories of this paper are: patt-sol
5
The categories of this paper are: gr-qc
30
The categories of this paper are: q-alg
10
The categories of this paper are: q-bio
36
The categories of this paper are: cmp-lg
6
The categories of this paper are: astro-ph
33
The categories of this paper are: plasm-ph
24
The categories of this paper are: dg-ga
14
The categories of this paper are: math-ph
23
The categories of this paper are: solv-int
13
The categories of this paper are: quant-ph
22
The categories of this paper are: chao-dyn
8
The categories of this paper are: hep-ex