1. Cardiac MRI Image Segmentation for Left Ventricle and Right Ventricle using Deep Learning
- Author
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Seo, Bosung, Mariano, Daniel, Beckfield, John, Madenur, Vinay, Hu, Yuming, Reina, Tony, Bobar, Marcus, Nguyen, Mai H., and Altintas, Ilkay
- Subjects
FOS: Computer and information sciences ,Computer Science - Machine Learning ,Image and Video Processing (eess.IV) ,FOS: Electrical engineering, electronic engineering, information engineering ,Electrical Engineering and Systems Science - Image and Video Processing ,Machine Learning (cs.LG) - Abstract
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 and RV) segmentation. Another aim of the project is to find a model that would be generalizable across medical imaging datasets. We utilized a variety of models, datasets, and tests to determine which one is well suited to this purpose. Specifically, we implemented three models (2-D U-Net, 3-D U-Net, and DenseNet), and evaluated them on four datasets (Automated Cardiac Diagnosis Challenge, MICCAI 2009 LV, Sunnybrook Cardiac Data, MICCAI 2012 RV). While maintaining a consistent preprocessing strategy, we tested the performance of each model when trained on data from the same dataset as the test data, and when trained on data from a different dataset than the test dataset. Data augmentation was also used to increase the adaptability of the models. The results were compared to determine performance and generalizability.
- Published
- 2019
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