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.

翻译：深度学习被广泛用于医学图像分割,大量论文被提交,记录了实地深层学习的成功。本文介绍了关于医学图像分割的全面专题调查,用深层学习技术对医学图像分割进行了全面专题调查。本文有两种原创贡献。首先,与将深层医学图像分割的文献直接分为许多群体的传统调查相比,我们根据从粗糙到细微的多层次结构对目前流行的文献进行了详细分类。第二,本文侧重于监管和监管薄弱的学习方法,而没有包括许多旧调查中引入的、目前不受欢迎的方法。关于监督的学习方法,我们从三个方面分析了文献:主干网络的选择、网络块的设计以及损失功能的改进。关于监管薄弱的学习方法,我们分别根据数据增强、传输学习和互动分离等方法对文献进行了调查。与现有调查相比,本调查对文献进行了非常不同的分类,对于读者来说更方便地理解相关原理,并将指导他们思考如何根据深层学习方法适当改进医学图像分割方法。