计算机科学
深度学习
循环神经网络
人工智能
最大值和最小值
卷积神经网络
模式识别(心理学)
特征提取
卷积(计算机科学)
特征(语言学)
帧(网络)
人工神经网络
计算机视觉
数学
数学分析
哲学
电信
语言学
作者
Fatemeh Taheri Dezaki,Zhibin Liao,Christina Luong,Hany Girgis,Neeraj Dhungel,Ali A. Abdi,Delaram Behnami,Ken Gin,Robert Rohling,Purang Abolmaesumi,Teresa Tsang
出处
期刊:IEEE Transactions on Medical Imaging
[Institute of Electrical and Electronics Engineers]
日期:2019-08-01
卷期号:38 (8): 1821-1832
被引量:56
标识
DOI:10.1109/tmi.2018.2888807
摘要
Accurate detection of end-systolic (ES) and end-diastolic (ED) frames in an echocardiographic cine series can be difficult but necessary pre-processing step for the development of automatic systems to measure cardiac parameters. The detection task is challenging due to variations in cardiac anatomy and heart rate often associated with pathological conditions. We formulate this problem as a regression problem and propose several deep learning-based architectures that minimize a novel global extrema structured loss function to localize the ED and ES frames. The proposed architectures integrate convolution neural networks (CNNs)-based image feature extraction model and recurrent neural networks (RNNs) to model temporal dependencies between each frame in a sequence. We explore two CNN architectures: DenseNet and ResNet, and four RNN architectures: long short-term memory, bi-directional LSTM, gated recurrent unit (GRU), and Bi-GRU, and compare the performance of these models. The optimal deep learning model consists of a DenseNet and GRU trained with the proposed loss function. On average, we achieved 0.20 and 1.43 frame mismatch for the ED and ES frames, respectively, which are within reported inter-observer variability for the manual detection of these frames.
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