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

By Phi Vu Tran. arXiv.org, 2016

342 citations recorded by the source corpus.

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 this https URL

Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/cf7a34b446dd01172be8739c7ef57d0a1e845d89

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In Biomedical Engineering

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In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved? (2018, background): “Tran et al. [16] developed a deep fully convolutional neural network architecture to segment both LV and RV structures.”
Medical Image Computing and Computer-Assisted Intervention − MICCAI 2017: 20th International Conference, Quebec City, QC, Canada, September 11-13, 2017, Proceedings, Part II (2017, neutral): “focussing on cell segmentation [15], detection and counting [8,10,16] or tracking [1,6,7,12,14], but very few deal with LFM data.”
Deep Learning for Cardiac Image Segmentation: A Review (2019, methodology): “Transfer learning can help to decrease the training time and achieve lower generalization error (85).”
A survey on deep learning in medical image analysis (2017, neutral): “Zhang et al. (2016a) Image reconstruction CT CNN Reconstructing from limited angle measurements,”
The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches (2018, neutral): “Cardiac Segmentation using short-Axis MRI [104].”
Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Segmentation: The M&Ms Challenge (2021, methodology): “cohorts [10], [11], but these works do not include methods for addressing domain shifts between training and new unseen”
Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation (2017, methodology): “APPROACH (ACNN-SEG) IS COMPARED AGAINST STATE-OF-THE-ART SLICE BY SLICE SEGMENTATION (2D-FCN [44]) METHOD, 3D-UNET”
A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises (2020, methodology): “One of the ﬁrst efforts to apply a fully convolutional network (FCN) [151] to segment the left ventricle (LV), myocardium and right ventricle from 2D short-axis cardiac magnetic resonance (MR) images was done by Tran [152], signiﬁcantly outperforming traditional methods in accuracy and speed.”
Semi-supervised Learning for Network-Based Cardiac MR Image Segmentation (2017, neutral): “For cardiac MR image segmentation, FCN and other network architectures have also been explored [1,9,11,16,17].”
3D deeply supervised network for automated segmentation of volumetric medical images (2017, methodology): “The latest adancements for cardiac segmentation were dedicated to CNNs hich hierarchically extract representations in a data-driven man- er, for examples, Tran (2016) employed fully convolutional netorks and Wolterink et al. (2016) utilized dilated convolutional eural networks.”
A Review of Deep-Learning-Based Medical Image Segmentation Methods (2021, methodology): “[87] applied a fully convolutional neural network architecture to pixel classification for cardiac magnetic resonance imaging.”
A review on deep learning in medical image analysis (2021, methodology): “The advantage of this model is generally used because of its great accuracy [104].”