A Fully Convolutional Neural Network for Cardiac Segmentation in Short-Axis MRI
This work addresses the problem of timely diagnosis and management of cardiac pathologies for medical professionals, representing an incremental advancement in applying existing deep learning architectures to a specific domain.
The authors tackled automated left and right ventricle segmentation in cardiac MRI by proposing a fully convolutional neural network, achieving robust outperformance over previous fully automated methods across multiple evaluation measures on various datasets.
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