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Dataset / Analytics Pipeline for Left and Right Ventricle Segmentation on Cardiac MRI using …

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Title
Analytics Pipeline for Left and Right Ventricle Segmentation on Cardiac MRI using Deep Learning
Date Created and/or Issued
2019-01-04 to 2019-06-07
Contributing Institution
UC San Diego, Research Data Curation Program
Collection
Data Science & Engineering Master of Advanced Study (DSE MAS) Capstone Projects
Rights Information
Under copyright
Constraint(s) on Use: This work is protected by the U.S. Copyright Law (Title 17, U.S.C.). Use of this work beyond that allowed by "fair use" or any license applied to this work requires written permission of the copyright holder(s). Responsibility for obtaining permissions and any use and distribution of this work rests exclusively with the user and not the UC San Diego Library. Inquiries can be made to the UC San Diego Library program having custody of the work.
Use: This work is available from the UC San Diego Library. This digital copy of the work is intended to support research, teaching, and private study.
Rights Holder and Contact
Beckfield, John; Hu, Yuming; Madenur, Vinay; Mariano, Daniel; Seo, Bosung
Description
This is the capstone project for MAS DSE program. The goal of the capstone project is to complete an end to end analysis of a large dataset with big data characteristics. The​ ​goal​ ​of​ ​this​ ​project​ ​is​ ​to​ use magnetic resonance imaging(MRI) data to provide an end-to-end analytics pipeline for left and right ventricle (LV & RV) segmentation. For the left ventricle, we map both the epicardium and endocardium surfaces. The endocardium segment is the area of the left ventricle contained by the inside of the wall of the left ventricle, and is the most reliable way to calculate the ejection fraction of the heart. The left ventricle epicardium segment is the area contained by the outside of the wall of the left ventricle, and once the contours of the inside and outside of the wall are known, the contours of the myocardium, the actual wall, can be calculated. The contours of the myocardium are used in detecting the severity of damage done by heart attacks. For the right ventricle, we segment the endocardium. Since the process of analyzing cardiac images is time consuming and labor intensive, our project aims to apply deep learning to this process to achieve consistent segmentation results efficiently. We also compare the performance of 2D UNET, 3D UNET and Densenet models across various datasets. The datasets used in this project are Sunnybrook Cardiac dataset, Automated Cardiac Diagnosis Challenge dataset, MICCAI 2012 Right Ventricle Segmentation challenge dataset and CAP 2011 Left Ventricle Segmentation challenge dataset.
Research Data Curation Program, UC San Diego, La Jolla, 92093-0175 (https://lib.ucsd.edu/rdcp)
Nguyen, Mai; Beckfield, John; Hu, Yuming; Madenur, Vinay; Mariano, Daniel; Seo, Bosung; Bobar, Marcus; Reina, Tony (2019). Analytics Pipeline for Left and Right Ventricle Segmentation on Cardiac MRI using Deep Learning. In Data Science & Engineering Master of Advanced Study (DSE MAS) Capstone Projects. UC San Diego Library Digital Collections. https://doi.org/10.6075/J0D21VXP
Type
dataset
Identifier
ark:/20775/bb47487939
Subject
Segmentation
Ventricle
DenseNet
Magnetic resonance imaging (MRI)
Cardiac
Keras (Python package)
Capstone projects
Deep learning
Data Science & Engineering Master of Advanced Study (DSE MAS)
Right ventricle (RV)
Left ventricle (LV)
U-NET
Neural networks (Computer science)
DSE MAS - 2019 Cohort

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