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基于心脏磁共振短轴电影图像的右心室分割新进展

  • 苏新宇 ,
  • 王丽嘉 ,
  • 聂生东 ,
  • 胡立伟 ,
  • 钟玉敏
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  • 1. 上海理工大学 医疗器械与食品学院, 上海 200093;
    2. 上海交通大学医学院附属上海儿童医学中心 影像诊断中心, 上海 200127

收稿日期: 2018-09-03

  网络出版日期: 2019-08-27

基金资助

上海市卫生和计划生育委员会科研课题(20164Y0150).

Progress of Right Ventricle Segmentation from Short-Axis Images Acquired with Cardiac Cine MRI

  • SU Xin-yu ,
  • WANG Li-jia ,
  • NIE Sheng-dong ,
  • HU Li-wei ,
  • ZHONG Yu-min
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  • 1. School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China;
    2. Diagnostic Imaging Center, Shanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China

Received date: 2018-09-03

  Online published: 2019-08-27

摘要

右心室分割对肺动脉高压、法洛四联症等疾病的心脏功能评估具有重要意义.然而,右心室结构复杂,变动性大、心肌薄且毗邻脂肪,实验全自动分割一直是难点.心脏磁共振短轴电影图像时空分辨高,常用于临床右心室分割及功能评价.本文基于心脏磁共振短轴电影图像对右心室分割方法进行了综述,首先回顾了传统右心室分割算法,然后重点介绍了基于多图谱和深度学习算法的右心室分割进展,并介绍了右心室分割结果常用的评估指标.通过上述回顾发现,基于深度学习算法的分割方法是今后临床应用的右心室分割的主要方法,对心脏相关疾病的诊断及预后十分重要,而且可大大提高医生的工作效率.

本文引用格式

苏新宇 , 王丽嘉 , 聂生东 , 胡立伟 , 钟玉敏 . 基于心脏磁共振短轴电影图像的右心室分割新进展[J]. 波谱学杂志, 2019 , 36(3) : 377 -391 . DOI: 10.11938/cjmr20182678

Abstract

Right ventricle (RV) segmentation is essential for assessing cardiac function in patients with pulmonary hypertension, tetralogy of Fallot, and so on. However, it remains difficult due to the complex structure of heart, thin myocardium and large variability of the RV, as well as the interferences from the fat nearby. Due to its high temporal and spatial resolution, cardiac cine magnetic resonance imaging is widely used for functional evaluation of the heart. This article reviews the commonly-used methods for RV segmentation from the cardiac cine magnetic resonance images. The traditional algorithms are described first, followed by the novel multi-atlas and deep learning methods. Lastly, the evaluation standards for RV segmentation are introduced. It is concluded that the deep learning-based segmentation methods may have the potential to become the method of choice in clinical settings due to their high efficiency and accuracy in the diagnosis and prognosis of the heart-related diseases.

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