CardiacFCN

CardiacFCN

Description
The proposed FCN architecture is efficiently trained end-to-end on a graphics processing unit (GPU) in a single learning stage from whole image inputs and ground truths to make inference at every pixel, a task commonly known as pixel-wise labeling or per-pixel classification.
Application
Cardiac Imaging
Task
Segmentation: Segmenting the right ventricle in MRI.
Type
Supervised learning
Architecture
Convolutional Neural Network (CNN)
Data
Magnetic Resonance (MRI): source
Title
A Fully Convolutional Neural Network for Cardiac Segmentation in Short-Axis MRI
Authors
Phi Vu Tran
Abstract
Automated cardiac segmentation from magnetic resonance imaging datasets is an essential step in the timely diagnosis and management of cardiac pathologies. We propose to tackle the problem of automated left and right ventricle segmentation through the application of a deep fully convolutional neural network architecture. Our model is efficiently trained end-to-end in a single learning stage from wholeimage inputs and ground truths to make inference at every pixel. To our knowledge, this is the first application of a fully convolutional neural network architecture for pixel-wise labeling in cardiac magnetic resonance imaging. Numerical experiments demonstrate that our model is robust to outperform previous fully automated methods across multiple evaluation measures on a range of cardiac datasets. Moreover, our model is fast and can leverage commodity compute resources such as the graphics processing unit to enable state-of-the-art cardiac segmentation at massive scales. The models and code are available at https://github.com/vuptran/cardiac-segmentation.
Year
2016
bibtex
@article{DBLP:journals/corr/Tran16, author = {Phi Vu Tran}, title = {A Fully Convolutional Neural Network for Cardiac Segmentation in Short-Axis {MRI}}, journal = {CoRR}, volume = {abs/1604.00494}, year = {2016}, url = {http://arxiv.org/abs/1604.00494}, archivePrefix = {arXiv}, eprint = {1604.00494}, timestamp = {Wed, 07 Jun 2017 14:41:57 +0200}, biburl = {http://dblp.org/rec/bib/journals/corr/Tran16}, bibsource = {dblp computer science bibliography, http://dblp.org} }
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License

Acknowledgements

Model License

Sample Data License