Tongue segmentation is an essential part of intelligent medicine diagnosis. The purpose is to generate an accurate contour of the tongue region by a precise mask. In recent years, deep learning methods have been widely applied in the field of image processing and have achieved impressive performance. With the increasing requirement of the performance for medical image segmentation, many scholars have employed deep learning to tongue segmentation. The methods of deep learning-based tongue segmentation are analyzed, classified and summarized. In the field of tongue segmentation applications, various tongue segmentation methods based on deep learning are divided into eight types: convolutional neural network (CNN), fully convolutional network (FCN), convolutional model with graphical model, encoder-decoder based model, regional convolutional network-based model, atrous convolutional model, transfer learning and other methods. This paper presents a comprehensive survey of the recently developed deep learning for tongue segmentation, and analyzes the strengths and weaknesses of these methods. The commonly used data sets and evaluation indexes of tongue segmentation based on deep learning are visually compared and quantitatively evaluated. This survey is concluded by discussing the potential development in future research work.