  Deep learning has been widely used for medical image segmentation and a large
number of papers has been presented recording the success of deep learning in
the field. In this paper, we present a comprehensive thematic survey on medical
image segmentation using deep learning techniques. This paper makes two
original contributions. Firstly, compared to traditional surveys that directly
divide literatures of deep learning on medical image segmentation into many
groups and introduce literatures in detail for each group, we classify
currently popular literatures according to a multi-level structure from coarse
to fine. Secondly, this paper focuses on supervised and weakly supervised
learning approaches, without including unsupervised approaches since they have
been introduced in many old surveys and they are not popular currently. For
supervised learning approaches, we analyze literatures in three aspects: the
selection of backbone networks, the design of network blocks, and the
improvement of loss functions. For weakly supervised learning approaches, we
investigate literature according to data augmentation, transfer learning, and
interactive segmentation, separately. Compared to existing surveys, this survey
classifies the literatures very differently from before and is more convenient
for readers to understand the relevant rationale and will guide them to think
of appropriate improvements in medical image segmentation based on deep
learning approaches.
