Analytics Pipeline for Left and Right Ventricle Segmentation on Cardiac MRI using Deep Learning
Project Readme
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Scope And Content | Description of how to preprocess the data, train the model, get predictions from the model, and post process the predictions |
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GitHub repository
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Scope And Content | Code to preprocess the data, train and predict from the model, and postprocessing predictions, as well as code to classify diagnosises and display results |
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Technical Details | Python with medpy, numpy, keras, matplotlib, flask and other libraries |
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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
- Description
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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.
- Creation Date
- 2019-01-04 to 2019-06-07
- Date Issued
- 2019
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- Related Resources
- Automated Cardiac Diagnosis Challenge Dataset: https://www.creatis.insa-lyon.fr/Challenge/acdc/databases.html
- Left Ventricle Segmentation Dataset: http://www.cardiacatlas.org/challenges/lv-segmentation-challenge/
- Right Ventricle Segmentation Dataset: https://pagesperso.litislab.fr/cpetitjean/mr-images-and-contour-data/
- Sunnybrook Dataset: http://www.cardiacatlas.org/studies/sunnybrook-cardiac-data/
- 2011 Left Ventricle Segmentation Challenge Dataset - Avan Suinesiaputra, Brett R. Cowan, Ahmed O. Al-Agamy, Mustafa A. Elattar, Nicholas Ayache, Ahmed S. Fahmy, Ayman M. Khalifa, Pau Medrano-Gracia, Marie-Pierre Jolly, Alan H. Kadish, Daniel C. Lee, Ján Margeta, Simon K. Warfield, Alistair A. Young. A collaborative resource to build consensus for automated left ventricular segmentation of cardiac MR images, Medical Image Analysis, Volume 18, Issue 1, 2014, Pages 50-62, ISSN 1361-8415. https://doi.org/10.1016/j.media.2013.09.001
- Automated Cardiac Diagnosis Challenge Dataset -O. Bernard, A. Lalande, C. Zotti, F. Cervenansky, et al. "Deep Learning Techniques for Automatic MRI Cardiac Multi-structures Segmentation and Diagnosis: Is the Problem Solved ?" in IEEE Transactions on Medical Imaging, vol. 37, no. 11, pp. 2514-2525, Nov. 2018. https://www.doi.org/10.1109/TMI.2018.2837502
- MICCAI 2012 Right Ventricle Segmentation Challenge Dataset - Caroline Petitjean, Maria A Zuluaga, Wenjia Bai, Jean-Nicolas Dacher, Damien Grosgeorge et al. Right Ventricle Segmentation From Cardiac MRI: A Collation Study Medical Image Analysis, Elsevier, 2014, pp.43. https://www.doi.org/10.1016/j.media.2014.10.004
- Sunnybrook Cardiac Dataset - Radau P, Lu Y, Connelly K, Paul G, Dick AJ, Wright GA. “Evaluation Framework for Algorithms Segmenting Short Axis Cardiac MRI.” The MIDAS Journal – Cardiac MR Left Ventricle Segmentation Challenge. https://hdl.handle.net/10380/3070
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- License
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Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License
- Rights Holder
- Beckfield, John; Hu, Yuming; Madenur, Vinay; Mariano, Daniel; Seo, Bosung
- Copyright
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Under copyright (US)
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.
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.
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Research Data Curation Program, UC San Diego, La Jolla, 92093-0175 (https://lib.ucsd.edu/rdcp)
- Last Modified
2023-06-30