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

TL;DR

This paper introduces a fully convolutional neural network for automated, pixel-wise segmentation of the left and right ventricles in cardiac MRI images, achieving high accuracy and efficiency.

Contribution

It is the first to apply a fully convolutional neural network architecture for cardiac MRI segmentation, enabling end-to-end training and inference from whole images.

Findings

Outperforms previous automated methods on multiple datasets

Robust and fast segmentation leveraging GPU resources

Available code facilitates reproducibility and further research

Abstract

Automated cardiac segmentation from magnetic resonance imaging datasets is an essential step in the timely diagnosis and management of cardiac pathologies. We propose to tackle the problem of automated left and right ventricle segmentation through the application of a deep fully convolutional neural network architecture. Our model is efficiently trained end-to-end in a single learning stage from whole-image inputs and ground truths to make inference at every pixel. To our knowledge, this is the first application of a fully convolutional neural network architecture for pixel-wise labeling in cardiac magnetic resonance imaging. Numerical experiments demonstrate that our model is robust to outperform previous fully automated methods across multiple evaluation measures on a range of cardiac datasets. Moreover, our model is fast and can leverage commodity compute resources such as the graphics processing unit to enable state-of-the-art cardiac segmentation at massive scales. The models and code are available at https://github.com/vuptran/cardiac-segmentation