An SVM Based Approach for Cardiac View Planning

07/11/2014

∙

We consider the problem of automatically prescribing oblique planes (short axis, 4 chamber and 2 chamber views) in Cardiac Magnetic Resonance Imaging (MRI). A concern with technologist-driven acquisitions of these planes is the quality and time taken for the total examination. We propose an automated solution incorporating anatomical features external to the cardiac region. The solution uses support vector machine regression models wherein complexity and feature selection are optimized using multi-objective genetic algorithms. Additionally, we examine the robustness of our approach by training our models on images with additive Rician-Gaussian mixtures at varying Signal to Noise (SNR) levels. Our approach has shown promising results, with an angular deviation of less than 15 degrees on 90 in terms of average 6-fold cross validation performance -- this is generally within acceptable bounds of variation as specified by clinicians.

READ FULL TEXT

An SVM Based Approach for Cardiac View Planning

A Fully Convolutional Neural Network for Cardiac Segmentation in Short-Axis MRI

A Combined Deep-Learning and Deformable-Model Approach to Fully Automatic Segmentation of the Left Ventricle in Cardiac MRI

CHMMOTv1 – Cardiac and Hepatic Multi-Echo (T2*) MRI Images and Clinical Dataset for Iron Overload on Thalassemia Patients

How well do U-Net-based segmentation trained on adult cardiac magnetic resonance imaging data generalise to rare congenital heart diseases for surgical planning?

Copy Number Variation Informs fMRI-based Prediction of Autism Spectrum Disorder

Segmentation and Classification of Cine-MR Images Using Fully Convolutional Networks and Handcrafted Features

An SVM Based Approach for Cardiac View Planning

Related Research

A Fully Convolutional Neural Network for Cardiac Segmentation in Short-Axis MRI

A Combined Deep-Learning and Deformable-Model Approach to Fully Automatic Segmentation of the Left Ventricle in Cardiac MRI

CHMMOTv1 – Cardiac and Hepatic Multi-Echo (T2*) MRI Images and Clinical Dataset for Iron Overload on Thalassemia Patients

How well do U-Net-based segmentation trained on adult cardiac magnetic resonance imaging data generalise to rare congenital heart diseases for surgical planning?

Copy Number Variation Informs fMRI-based Prediction of Autism Spectrum Disorder

Segmentation and Classification of Cine-MR Images Using Fully Convolutional Networks and Handcrafted Features