Automatic myocardial segmentation in dynamic contrast enhanced perfusion MRI using Monte Carlo dropout in an encoder-decoder convolutional neural network

Kim Yoon-Chul; Kim Khu Rai; Choe Yeon Hyeon

首页> 外文期刊>Computer Methods and Programs in Biomedicine: An International Journal Devoted to the Development, Implementation and Exchange of Computing Methodology and Software Systems in Biomedical Research and Medical Practice >Automatic myocardial segmentation in dynamic contrast enhanced perfusion MRI using Monte Carlo dropout in an encoder-decoder convolutional neural network

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Automatic myocardial segmentation in dynamic contrast enhanced perfusion MRI using Monte Carlo dropout in an encoder-decoder convolutional neural network

机译：在编码器 - 解码器卷积神经网络中使用Monte Carlo辍学的动态对比度增强灌注MRI的自动心肌细分

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Background and Objective: Cardiac perfusion magnetic resonance imaging (MRI) with first pass dynamic contrast enhancement (DCE) is a useful tool to identify perfusion defects in myocardial tissues. Automatic segmentation of the myocardium can lead to efficient quantification of perfusion defects. The purpose of this study was to investigate the usefulness of uncertainty estimation in deep convolutional neural networks for automatic myocardial segmentation.

机译：背景和目的：心脏灌注磁共振成像（MRI）具有首次通过动态对比增强（DCE）是一种识别心肌组织中灌注缺陷的有用工具。心肌的自动分割可导致灌注缺陷的有效量化。本研究的目的是研究自动心肌细分的深度卷积神经网络中不确定性估计的有用性。

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《Computer Methods and Programs in Biomedicine: An International Journal Devoted to the Development, Implementation and Exchange of Computing Methodology and Software Systems in Biomedical Research and Medical Practice》 |2020年第2020期|共9页
作者
Kim Yoon-Chul; Kim Khu Rai; Choe Yeon Hyeon;
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作者单位

Sungkyunkwan Univ Samsung Med Ctr Clin Res Inst Sch Med Seoul South Korea;

Sogang Univ Dept Elect Engn Seoul South Korea;

Sungkyunkwan Univ Sch Med Dept Radiol 81 Ilwon Ro Seoul 06351 South Korea;

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原文格式 PDF
正文语种 eng
中图分类计算技术、计算机技术;
关键词
MRI; Heart; Perfusion; Segmentation; Convolutional neural network;

机译：MRI;心脏;灌注;分割;卷积神经网络;

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1. Automatic myocardial segmentation in dynamic contrast enhanced perfusion MRI using Monte Carlo dropout in an encoder-decoder convolutional neural network [J] . Kim Yoon-Chul, Kim Khu Rai, Choe Yeon Hyeon Computer Methods and Programs in Biomedicine: An International Journal Devoted to the Development, Implementation and Exchange of Computing Methodology and Software Systems in Biomedical Research and Medical Practice . 2020,第期

机译：在编码器 - 解码器卷积神经网络中使用Monte Carlo辍学的动态对比度增强灌注MRI的自动心肌细分
2. Automatic segmentation of prostate MRI using convolutional neural networks: Investigating the impact of network architecture on the accuracy of volume measurement and MRI-ultrasound registration [J] . Ghavami Nooshin, Hu Yipeng, Gibson Eli, Medical image analysis . 2019,第期

机译：使用卷积神经网络自动分割前列腺MRI：调查网络架构对体积测量准确性的影响和MRI超声登记
3. Model-free prostate cancer segmentation from dynamic contrast-enhanced MRI with recurrent convolutional networks: A feasibility study [J] . Peter Q. Lee, Alessro Guida, Steve Patterson, Computerized Medical Imaging and Graphics: The Official Jounal of the Computerized Medical Imaging Society . 2019,第期

机译：动态对比增强MRI的无模型前列腺癌细分与经常性卷积网络：可行性研究
4. A semi-automatic software package for analysis of dynamic contrast-enhanced MRI myocardial perfusion studies [C] . Pack N.A., Vijayakumar S., Kim T.H., Computers in Cardiology, 2009 . 2009

机译：半自动软件包，用于分析动态对比增强的MRI心肌灌注研究
5. Assessment of pulmonary perfusion using T1-weighted dynamic contrast enhanced MRI. [D] . Bell, Laura C. 2015

机译：使用T1加权动态对比增强MRI评估肺灌注。
6. Convolutional Neural Networks for Direct Inference of Pharmacokinetic Parameters: Application to Stroke Dynamic Contrast-Enhanced MRI [O] . Cagdas Ulas, Dhritiman Das, Michael J. Thrippleton, 2018

机译：卷积神经网络用于药代动力学参数的直接推论：在脑卒中动态对比增强MRI中的应用
7. Optimal input configuration of dynamic contrast enhanced MRI in convolutional neural networks for liver segmentation [O] . Mariëlle J. A. Jansen, Hugo J. Kuijf, Josien P. W. Pluim 2019

机译：肝脏分割卷积神经网络中动态对比增强MRI的最佳输入配置

Automatic myocardial segmentation in dynamic contrast enhanced perfusion MRI using Monte Carlo dropout in an encoder-decoder convolutional neural network

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