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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 References: 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, http://hdl.handle.net/10380/3070
Type
Dataset
Subject
Segmentation Ventricle DenseNet Magnetic resonance imaging (MRI) Cardiac Keras (Python package) Neural network Capstone projects Deep learning Data Science & Engineering Master of Advanced Study (DSE MAS) Right ventricle (RV) U-NET Left ventricle (LV) DSE MAS - 2019 Cohort
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