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Article

Fully Automated 3D Cardiac MRI Localisation and Segmentation Using Deep Neural Networks

1
Pattern Recognition Lab, Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg, 91052 Erlangen, Germany
2
Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), School of Computing, LICAMM Leeds Institute of Cardiovascular and Metabolic Medicine, School of Medicine, University of Leeds, Leeds LS2 9JT, UK
*
Author to whom correspondence should be addressed.
Received: 23 April 2020 / Revised: 30 June 2020 / Accepted: 1 July 2020 / Published: 6 July 2020
Cardiac magnetic resonance (CMR) imaging is used widely for morphological assessment and diagnosis of various cardiovascular diseases. Deep learning approaches based on 3D fully convolutional networks (FCNs), have improved state-of-the-art segmentation performance in CMR images. However, previous methods have employed several pre-processing steps and have focused primarily on segmenting low-resolutions images. A crucial step in any automatic segmentation approach is to first localize the cardiac structure of interest within the MRI volume, to reduce false positives and computational complexity. In this paper, we propose two strategies for localizing and segmenting the heart ventricles and myocardium, termed multi-stage and end-to-end, using a 3D convolutional neural network. Our method consists of an encoder–decoder network that is first trained to predict a coarse localized density map of the target structure at a low resolution. Subsequently, a second similar network employs this coarse density map to crop the image at a higher resolution, and consequently, segment the target structure. For the latter, the same two-stage architecture is trained end-to-end. The 3D U-Net with some architectural changes (referred to as 3D DR-UNet) was used as the base architecture in this framework for both the multi-stage and end-to-end strategies. Moreover, we investigate whether the incorporation of coarse features improves the segmentation. We evaluate the two proposed segmentation strategies on two cardiac MRI datasets, namely, the Automatic Cardiac Segmentation Challenge (ACDC) STACOM 2017, and Left Atrium Segmentation Challenge (LASC) STACOM 2018. Extensive experiments and comparisons with other state-of-the-art methods indicate that the proposed multi-stage framework consistently outperforms the rest in terms of several segmentation metrics. The experimental results highlight the robustness of the proposed approach, and its ability to generate accurate high-resolution segmentations, despite the presence of varying degrees of pathology-induced changes to cardiac morphology and image appearance, low contrast, and noise in the CMR volumes. View Full-Text
Keywords: cardiac MRI; deep neural network; CNN; multistage segmentation; MRI segmentation; cardiovascular diseases cardiac MRI; deep neural network; CNN; multistage segmentation; MRI segmentation; cardiovascular diseases
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MDPI and ACS Style

Vesal, S.; Maier, A.; Ravikumar, N. Fully Automated 3D Cardiac MRI Localisation and Segmentation Using Deep Neural Networks. J. Imaging 2020, 6, 65. https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging6070065

AMA Style

Vesal S, Maier A, Ravikumar N. Fully Automated 3D Cardiac MRI Localisation and Segmentation Using Deep Neural Networks. Journal of Imaging. 2020; 6(7):65. https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging6070065

Chicago/Turabian Style

Vesal, Sulaiman, Andreas Maier, and Nishant Ravikumar. 2020. "Fully Automated 3D Cardiac MRI Localisation and Segmentation Using Deep Neural Networks" Journal of Imaging 6, no. 7: 65. https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging6070065

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