传统方法和深度学习用于不同模态心脏医学图像的分割研究进展
收稿日期: 2023-10-19
网络出版日期: 2023-12-27
Research Progress on Cardiac Segmentation in Different Modal Medical Images by Traditional Methods and Deep Learning
Received date: 2023-10-19
Online published: 2023-12-27
随着老龄化加剧，心血管疾病患病人数逐年增加，借助医学图像实现心脏功能的评估在诊疗过程中起着重要作用．心脏分割是评估心脏功能的前提，一直受到临床医生和科学研究者的密切关注．本文从传统方法和深度学习方法角度梳理了近十年以来关于心脏分割研究的文献．重点介绍了基于主动轮廓和图谱模型的传统分割方法，以及基于U-Net和全卷积神经网络（FCN）的深度学习算法．其中针对通过增加局部模块、优化损失函数、强化网络结构等方式改进深度学习网络以实现心脏特定区域精准分割这一主题进行了详细展开，并从心脏磁共振、X射线计算机断层扫描（CT）和超声3种成像模态对上述方法进行总结．最后总结了该领域目前的研究现状并对未来研究方向进行了展望．
常博 , 孙灏芸 , 高清宇 , 王丽嘉 . 传统方法和深度学习用于不同模态心脏医学图像的分割研究进展[J]. 波谱学杂志, 2024 , 41(2) : 224 -244 . DOI: 10.11938/cjmr20233086
As the aging population increases, the prevalence of cardiovascular disease rises annually. In this context, the evaluation of cardiac function using medical imaging techniques plays a pivotal role in the diagnosis and treatment of cardiovascular disease. Cardiac segmentation is a prerequisite for assessing cardiac function and has been closely studied by clinicians and scientific researchers. This paper provides a comprehensive review of the literature from the past decade on cardiac segmentation, categorizing the studies into traditional segmentation approaches and deep learning methodologies. Emphasis is placed on the detailed discussion of segmentation methods based on active contours and atlas models; deep learning algorithms based on U-Net and full convolution neural network (FCN) are also extensively discussed. In particular, this paper elaborates various approaches to enhance deep learning networks and achieve accurate segmentation of specific cardiac regions. These approaches include incorporating local modules, optimizing loss functions, and enhancing network architectures. A comprehensive summary of the aforementioned methods is presented, considering three imaging modalities: cardiac magnetic resonance imaging, computed tomography, and ultrasonic cardiogram. Lastly, the article concludes by summarizing the current research status and discussing research directions for further exploration.
Key words： cardiac image segmentation; deep learning; U-Net; FCN
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