深度学习的舌体分割研究综述

Survey of Tongue Segmentation in Deep Learning

舌体分割是智能医学诊断的重要组成部分,其目的是通过分割舌诊图像生成精准的舌体轮廓。近年来,深度学习方法在图像处理领域得到了广泛的应用并取得了较好的结果。随着医学图像分割对性能的要求越来越高,许多研究人员将深度学习运用到舌体分割中。主要对基于深度学习的舌体分割方法研究现状进行分析梳理和归纳总结。在舌体分割应用领域中,以各种深度学习方法作为研究对象,将基于深度学习的舌体分割方法划分为卷积神经网络(CNN)、全卷积网络(FCN)、卷积模型与图形模型、基于编解码器的模型、基于区域卷积网络模型、扩张卷积模型结构、迁移学习以及其他方法。在每类方法中,针对其改进和扩展的研究成果进行了全面的论述,总结分析其优势与不足;并对基于深度学习的舌体分割常用的数据集和评价指标进行了视觉比较与性能评估;最后讨论了未来研究工作中的发展潜力。

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 segmen-tation.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.