  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.
