医学图像图深度学习分割算法综述

Systematic Review on Graph Deep Learning in Medical Image Segmentation

精准分割医学图像中的器官或病灶,是医学图像智能分析领域的重要难题,其在临床上对于疾病的辅助诊疗有着重要应用价值。在解决医学图像信息表征及对非欧空间生理组织结构准确建模等挑战性问题方面,基于图深度学习的医学图像分割技术取得了重要突破,展现出显著的信息特征提取及表征优势,可获得更为精准的分割结果,已成为该领域新兴研究热点。为更好促进医学图像图深度学习分割算法的研究发展,对该领域的技术进展及应用现状做了系统的梳理总结。介绍了图的定义及图卷积网络的基本结构,详细阐述了谱图卷积和空域图卷积操作。根据GCN结合残差模块、注意力机制模块及学习模块三种技术结构模式,归纳并总结了其在医学图像分割中的研究进展。对图深度学习算法在医学图像分割领域的应用和发展做了概要总结和展望,为该领域的技术发展提供参考和新的研究思路。

High precision segmentation of organs or lesions in medical image is a vital challenge issue for intelligent analysis of medical image,it has important clinical application value for auxiliary diagnosis and treatment of diseases.Recently,in solving challenging problems such as medical image information representation and accurate modeling of non-Euclidean spatial physiological tissue structures,the graph deep learning based medical image segmentation technology has made important breakthroughs,and it has shown significant information feature extraction and characterization advantages.The merged technology also can obtain more accurate segmentation results,which has become an emerging research hotspot in this field.In order to better promote the research and development of the deep learning segmentation algorithm for medical image graphs,this paper makes a systematic summary of the technological progress and application status in this field.The paper introduces the definition of graphs and the basic structure of graph convolutional networks,and elaborates on spectral graph convolution and spatial graph convolution operations.Then,according to the three technical structure modes of GCN combined with residual module,attention mechanism module and learning module,the research progress in medical image segmentation has been encapsulated.The application and development of graph deep learning algorithms based medical image segmentation are summarized and prospected to provide references and guiding principles for the technical development of related researches.