  Accurate segmentation of multiple organs of the head, neck, chest, and
abdomen from medical images is an essential step in computer-aided diagnosis,
surgical navigation, and radiation therapy. In the past few years, with a
data-driven feature extraction approach and end-to-end training, automatic deep
learning-based multi-organ segmentation method has far outperformed traditional
methods and become a new research topic. This review systematically summarizes
the latest research in this field. For the first time, from the perspective of
full and imperfect annotation, we comprehensively compile 161 studies on deep
learning-based multi-organ segmentation in multiple regions such as the head
and neck, chest, and abdomen, containing a total of 214 related references. The
method based on full annotation summarizes the existing methods from four
aspects: network architecture, network dimension, network dedicated modules,
and network loss function. The method based on imperfect annotation summarizes
the existing methods from two aspects: weak annotation-based methods and semi
annotation-based methods. We also summarize frequently used datasets for
multi-organ segmentation and discuss new challenges and new research trends in
this field.
