Published on Tue Sep 17 2019

Cardiac MRI Image Segmentation for Left Ventricle and Right Ventricle using Deep Learning

Bosung Seo, Daniel Mariano, John Beckfield, Vinay Madenur, Yuming Hu, Tony Reina, Marcus Bobar, Mai H. Nguyen, Ilkay Altintas

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 segmentation. We utilized a variety of models,datasets, and tests to determine which one is well suited to this purpose.

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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.

Fri Sep 14 2018
Machine Learning
Left Ventricle Segmentation and Volume Estimation on Cardiac MRI using Deep Learning
In the United States, heart disease is the leading cause of death for both men and women, accounting for 610,000 deaths each year. Physicians use Magnetic Resonance Imaging (MRI) scans to take images of the heart in order to estimate its structural and functional parameters. In this work, various image preprocessing techniques, model configurations, and postprocessing methods are investigated.
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Fri Jul 23 2021
Computer Vision
Deep Learning Based Cardiac MRI Segmentation: Do We Need Experts?
Deep learning methods are the de-facto solutions to a multitude of medical image analysis tasks. Cardiac MRI segmentation is one such application which requires a large number of annotated data so a trained network can generalize well.
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Wed Sep 13 2017
Computer Vision
An Exploration of 2D and 3D Deep Learning Techniques for Cardiac MR Image Segmentation
Accurate segmentation of the heart is an important step towards evaluating cardiovascular function. We present a fully automated framework for segmenting the left (LV) and right (RV) ventricular cavities and the myocardium (Myo) on short-axis cardiac MR images.
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Mon Feb 13 2017
Computer Vision
Estimation of the volume of the left ventricle from MRI images using deep neural networks
Segmenting human left ventricle (LV) in magnetic resonance imaging (MRI) and calculating its volume are important for diagnosing cardiac diseases. In 2016, Kaggle organized a competition to estimate the volume of LV from MRI images.
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Fri Dec 25 2015
Computer Vision
A Combined Deep-Learning and Deformable-Model Approach to Fully Automatic Segmentation of the Left Ventricle in Cardiac MRI
The method employs deep learning algorithms to learn the segmentation task from the ground true data. Convolutional networks are employed to detect the LV chamber in MRI dataset. Stacked autoencoders are utilized to infer the shape of the LV.
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Sat Nov 09 2019
Machine Learning
Deep learning for cardiac image segmentation: A review
Deep learning has become the most widely used approach for cardiac image segmentation in recent years. This paper provides a review of over 100 papers using deep learning. We discuss the challenges and limitations with current deep learning-based approaches and suggest potential directions for future research.
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