According to the World Health Organization, cardiovascular diseases are the leading cause of death worldwide, accounting for 17.3 million deaths per year, a number that is expected to grow to more than 23.6 million by 2030. Most cardiac pathologies involve the left ventricle; therefore, estimation of several functional parameters from a previous segmentation of this structure can be helpful in diagnosis. Manual delineation is a time consuming and tedious task that is also prone to high intra and inter-observer variability. Thus, there exists a need for automated cardiac segmentation method to help facilitate the diagnosis of cardiovascular diseases.
LVNet is a deep fully convolutional neural network architecture to address this issue and assess its performance. The model was trained end to end in a supervised learning stage from whole cardiac MRI images input and ground truth to make a per pixel classification.
On our paper we use Caffe framework. For the present code Tensorflow deep learning framework was used over an NVidia Quadro K4200 Graphics Processing Unit. Others Nvidia GPU souch as GTX750 can be used. Also CPU can be used but the training will take a long time.
The net architecture is: Conv64-ReLU (2x) – MaxPooling – Conv128-ReLU (2x) – MaxPooling – Conv256-ReLU (2x) – MaxPooling – Conv512-ReLu-Dropout (2x) – Conv2-ReLU – Deconv – Crop – Softmax.
Training and testing processes were carried out using 5-fold cross validation with short axis cardiac magnetic resonance images from Sunnybrook Database.
On our paper (issing Caffe framework) we report a Dice score of 0.92 and 0.90, Hausdorff distance of 4.48 and 5.43, Jaccard index of 0.97 and 0.97, sensitivity of 0.92 and 0.90 and specificity of 0.99 and 0.99, overall mean values with SGD and RMSProp, respectively. The tensorflow version of the LVNet present similars results.
Caffe version of LVNet will be updated very soon here on GitHub.
Keywords: Image segmentation, medical image processing, cardiac MRI, Convolutional Neural Networks, deep learning.
Some examples of obtained left ventricle using LVNet, the green contour represents the results of proposed method while the red represents the ground truth contour.
- Liset Vázquez Romaguera ; Francisco Perdigón Romero ; Marly Guimarães Fernandes Costa and Cicero Ferreira Fernandes Costa Filho " Left ventricle segmentation in cardiac MRI images using fully convolutional neural networks ", Proc. SPIE 10134, Medical Imaging 2017: Computer-Aided Diagnosis, 101342Z (March 3, 2017);doi:10.1117/12.2253901
When citing LVNet in academic papers and thesis, please use this BibTeX entry:
@proceeding{doi:10.1117/12.2253901,
author = {Vázquez Romaguera, Liset and Romero, Francisco Perdigón and Costa, Marly Guimarães Fernandes and Costa Filho, Cicero Ferreira Fernandes},
title = {Left ventricle segmentation in cardiac MRI images using fully convolutional neural networks},
journal = {Proc. SPIE},
volume = {10134},
number = {},
pages = {101342Z-101342Z-11},
year = {2017},
doi = {10.1117/12.2253901},
URL = { http://dx.doi.org/10.1117/12.2253901},
eprint = {}
}
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Copyright (c) 2017 Liset Vázquez Romaguera, Francisco Perdigón Romero
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