基于密集多尺度U-net网络的电影心脏磁共振图像右心室自动分割

doi:10.11938/cjmr20192794

波谱学杂志 ›› 2020, Vol. 37 ›› Issue (4): 456-468.doi: 10.11938/cjmr20192794

基于密集多尺度U-net网络的电影心脏磁共振图像右心室自动分割

刘鹏¹, 钟玉敏², 王丽嘉¹

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放射科, 上海 200127

收稿日期:2019-12-16 出版日期:2020-12-05 发布日期:2020-03-12
通讯作者: 王丽嘉,Tel:021-55271173,E-mail:lijiawangmri@163.com. E-mail:lijiawangmri@163.com
基金资助:
上海市科委科技基金资助项目（17411953300）．

Automatic Segmentation of Right Ventricle in Cine Cardiac Magnetic Resonance Image Based on a Dense and Multi-Scale U-net Method

LIU Peng¹, ZHONG Yu-min², WANG Li-jia¹

1. School of Medical Instrument and Food Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China;
2. Department of Radiology, Shanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200127, China

Received:2019-12-16 Online:2020-12-05 Published:2020-03-12

1. 补充材料.pdf(607KB)

摘要/Abstract

摘要： 右心室分割对心脏功能评估具有重要意义.然而，右心室结构复杂，传统分割方法效果欠佳.本文提出一种密集多尺度U-net（DMU-net）网络用于分割右心室，首先对56例数据进行归一化、增强及感兴趣区域提取的预处理；然后结合多尺度融合和嵌套密集连接结构搭建网络；最后利用预处理后的数据对DMU-net网络进行训练和验证，并对15例仅提取感兴趣区域的数据进行测试.本文方法与手动分割的Dice系数和豪斯多夫距离平均值分别为0.862和4.44 mm，优于文献中其它分割效果较好的方法；舒张末期容积、收缩末期容积、射血分数及每搏输出量的相关系数为0.992、0.960、0.987和0.982.结果表明，使用本文方法的分割结果与手动分割结果重合度高、差异性小，有望为心脏疾病诊断提供参考.

关键词: 心脏磁共振图像, 右心室分割, 深度学习, 密集多尺度U-net

Abstract: Evaluating cardiac function from magnetic resonance images requires segmentation of the right ventricle (RV). The traditional segmentation methods, however, are often ineffective due to the complex structure of RV. Herein, a dense and multi-scale U-net (DMU-net) method is proposed to segment RV. Fifty six datasets were preprocessed, and the steps included normalization, enhancement and region of interest (ROI) extraction. Then a DMU-net was constructed by combining multi-scale aggregation with nested dense connection. The preprocessed datasets were then used to train and verify the DMU-net for predicting the results in 15 datasets with ROI extraction only. The mean of Dice index and Hausdorff distance obtained with the proposed method were 0.862 and 4.44 mm, respectively, which were better than those obtained with the conventional methods. Correlation coefficients of the endo-diastolic volume, endo-systolic volume, ejection fraction and stroke volume estimated were 0.992, 0.960, 0.987 and 0.982, respectively. The results demonstrated that the segmentation method based on the DMU-net has better relevance and consistency compared to manual segmentation, providing a promising tool the diagnosis of cardiac diseases.

Key words: cardiac magnetic resonance image, right ventricle segmentation, deep learning, dense and multi-scale U-net

中图分类号:

刘鹏, 钟玉敏, 王丽嘉. 基于密集多尺度U-net网络的电影心脏磁共振图像右心室自动分割[J]. 波谱学杂志, 2020, 37(4): 456-468.

LIU Peng, ZHONG Yu-min, WANG Li-jia. Automatic Segmentation of Right Ventricle in Cine Cardiac Magnetic Resonance Image Based on a Dense and Multi-Scale U-net Method[J]. Chinese Journal of Magnetic Resonance, 2020, 37(4): 456-468.

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基于密集多尺度U-net网络的电影心脏磁共振图像右心室自动分割

Automatic Segmentation of Right Ventricle in Cine Cardiac Magnetic Resonance Image Based on a Dense and Multi-Scale U-net Method

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