Segmentation of Fetal Left Ventricle in Echocardiographic Sequences Based on Dynamic Convolutional Neural Networks

2017 ◽  
Vol 64 (8) ◽  
pp. 1886-1895 ◽  
Author(s):  
Li Yu ◽  
Yi Guo ◽  
Yuanyuan Wang ◽  
Jinhua Yu ◽  
Ping Chen
Keyword(s):  
Neural Networks ◽  
Left Ventricle ◽  
Convolutional Neural Networks

Left ventricle segmentation in cardiac MRI images using fully convolutional neural networks

2017 ◽  
Author(s):  
Liset Vázquez Romaguera ◽  
Marly Guimarães Fernandes Costa ◽  
Francisco Perdigón Romero ◽  
Cicero Ferreira Fernandes Costa Filho
Keyword(s):  
Neural Networks ◽  
Left Ventricle ◽  
Convolutional Neural Networks ◽  
Cardiac Mri ◽  
Fully Convolutional Neural Networks ◽  
Ventricle Segmentation

scholarly journals Automatic segmentation of the left ventricle in echocardiographic images using convolutional neural networks

2021 ◽  
Vol 11 (5) ◽  
pp. 1763-1781
Author(s):  
Taeouk Kim ◽  
Mohammadali Hedayat ◽  
Veronica V. Vaitkus ◽  
Marek Belohlavek ◽  
Vinayak Krishnamurthy ◽  
...  
Keyword(s):  
Neural Networks ◽  
Left Ventricle ◽  
Convolutional Neural Networks ◽  
Automatic Segmentation ◽  
Echocardiographic Images

scholarly journals An Explainable Convolutional Neural Networks for Automatic Segmentation of the Left Ventricle in Cardiac MRI

2021 ◽  
Author(s):  
Jun Liu ◽  
Feng Deng ◽  
Geng Yuan ◽  
Xue Lin ◽  
Houbing Song ◽  
...  
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Medical Imaging ◽  
Left Ventricle ◽  
Convolutional Neural Networks ◽  
Cardiac Mri ◽  
Large Scale ◽  
Data Science ◽  
Training System ◽  
Data Sets

Recently, the study on model interpretability has become a hot topic in deep learning research area. Especially in the field of medical imaging, the requirements for safety are extremely high; Moreover, it is very important for the model to be able to explain. However, the existing solutions for left ventricular segmentation by convolutional neural networks are black boxes; explainable CNNs remains a challenge; explainable deep learning models has always been a task often overlooked in the entire data science lifecycle by data scientists or deep learning engineers. Because of very limited medical imaging data, most solutions currently use transfer learning methods to transfer the model which used on large-scale benchmark data sets (such as ImageNet) to fine tune medical imaging models. Consequently, a large amount of useless parameters are generated, resulting in further barrier for the model to provide a convincing explanation. This paper presents a novel method to automatically segment the Left Ventricle in Cardiac MRI by explainable convolutional neural networks with optimized size and parameters by our enhanced Deep Learning GPU Training System. It is very suitable for deployment on mobile devices. We simplify deep learning tasks on DIGITS systems, monitoring performance, and displaying the heat map of each layer of the network with advanced visualizations in real time. Our experiment results demonstrated that the proposed method is feasible and efficient.

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