NDLI: Deep convolutional neural networks for automatic segmentation of left ventricle cavity from cardiac magnetic resonance images

Content Provider	IET Digital Library
Author	Yang, Xulei Zeng, Zeng Yi, Su
Abstract	This work conducts a feasibility study of deep learning approaches for automatic segmentation of left ventricle (LV) cavity from cardiac magnetic resonance (CMR) images. Automatic LV cavity segmentation is a challenging task, partially due to the small size of the object as compared to the large CMR image background, especially at the apex. To cater for small object segmentation, the authors present a localisation-segmentation framework, to first locate the object in the large full image, then segment the object within the small cropped region of interest. The localisation is performed by a deep regression model based on convolutional neural networks, while the segmentation is done by the deep neural networks based on U-Net architecture. They also employ the Dice loss function for the training process of the segmentation models, to investigate its effects on the segmentation performance. The deep learning models are trained and evaluated by using public endocardium-annotated CMR datasets from York University and MICCAI 2009 LV Challenge websites. The average dice metric values of the authors’ proposed framework are 0.91 and 0.93, respectively, on these two databases. These results are promising as compared to the best results achieved by the current state-of-art, which shows the potentials of deep learning approaches for this particular application.
Starting Page	643
Ending Page	649
Page Count	7
ISSN	17519632
Volume Number	11
e-ISSN	17519640
Issue Number	Issue 8, Dec (2017)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/11/8
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2016.0482
Journal	IET Computer Vision
Publisher Date	2017-06-28
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Automatic Left Ventricle Cavity Segmentation Framework Biology And Medical Computing Biomedical Magnetic Resonance Imaging Biomedical MRI Cardiac Magnetic Resonance Image Cardiovascular System Computer Vision And Image Processing Technique Convolution Deep Convolutional Neural Network Deep Learning Approach Deep Neural Network Based U-Net Architecture Dice Loss Function Image Segmentation Knowledge Engineering Technique Learning in AI Localisation-segmentation Framework Medical Image Processing Medical Magnetic Resonance Imaging And Spectroscopy Neural Nets Object Detection Object Segmentation Framework Optical, Image And Video Signal Processing Patient Diagnostic Method And Instrumentation Public Endocardium-annotated CMR Datasets Regression Analysis Regression Model Based Convolutional Neural Network Spectroscopy
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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