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

2016/04/02 by Phi Vu Tran, Tran, Phi Vu · 8 citations
Computer Science · Engineering · Medicine · #Advanced MRI Techniques and Applications #Advanced X-ray and CT Imaging #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Medical Image Segmentation Techniques #cs.CV

paper · pdf · doi:10.48550/arxiv.1604.00494

Initial Technical Report; Include link to models and code

openalex publication_date 2016/04/02 · arxiv created 2017/04/27 · arxiv updated 2017/04/28 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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

Citations

• THE DISTRIBUTION OF THE FLORA IN THE ALPINE ZONE.1
• ImageNet classification with deep convolutional neural networks
• Deep Residual Learning for Image Recognition

Cited by

• Pancakes: Consistent Multi-Protocol Image Segmentation Across Biomedical Domains
• Convolutional Feature Noise Reduction for 2D Cardiac MR Image Segmentation
• PCA-aided Fully Convolutional Networks for Semantic Segmentation of Multi-channel fMRI
• Automatic Segmentation and Disease Classification Using Cardiac Cine MR Images
• 3D Consistent & Robust Segmentation of Cardiac Images by Deep Learning with Spatial Propagation
• A survey on incorporating domain knowledge into deep learning for medical image analysis
• Statistical Atlases and Computational Models of the Heart. ACDC and MMWHS Challenges
• Direct Multitype Cardiac Indices Estimation via Joint Representation and Regression Learning

Related

• An Exploration of 2D and 3D Deep Learning Techniques for Cardiac MR Image Segmentation
• 2D-3D Fully Convolutional Neural Networks for Cardiac MR Segmentation
• Heart Segmentation From MRI Scans Using Convolutional Neural Network
• Left Ventricle Segmentation in Cardiac MR Images Using Fully Convolutional Network
• Detection and segmentation of the Left Ventricle in Cardiac MRI using Deep Learning