Vesal S, Ravikumar N, Maier A (2019)
Publication Type: Conference contribution
Publication year: 2019
Publisher: Springer Verlag
Book Volume: 11395 LNCS
Pages Range: 319-328
Conference Proceedings Title: STACOM 2018: Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges
DOI: 10.1007/978-3-030-12029-0_35
Segmentation of the left atrial chamber and assessing its morphology, are essential for improving our understanding of atrial fibrillation, the most common type of cardiac arrhythmia. Automation of this process in 3D gadolinium enhanced-MRI (GE-MRI) data is desirable, as manual delineation is time-consuming, challenging and observer-dependent. Recently, deep convolutional neural networks (CNNs) have gained tremendous traction and achieved state-of-the-art results in medical image segmentation. However, it is difficult to incorporate local and global information without using contracting (pooling) layers, which in turn reduces segmentation accuracy for smaller structures. In this paper, we propose a 3D CNN for volumetric segmentation of the left atrial chamber in LGE-MRI. Our network is based on the well known U-Net architecture. We employ a 3D fully convolutional network, with dilated convolutions in the lowest level of the network, and residual connections between encoder blocks to incorporate local and global knowledge. The results show that including global context through the use of dilated convolutions, helps in domain adaptation, and the overall segmentation accuracy is improved in comparison to a 3D U-Net.
APA:
Vesal, S., Ravikumar, N., & Maier, A. (2019). Dilated Convolutions in Neural Networks for Left Atrial Segmentation in 3D Gadolinium Enhanced-MRI. In STACOM 2018: Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges (pp. 319-328). Springer Verlag.
MLA:
Vesal, Sulaiman, Nishant Ravikumar, and Andreas Maier. "Dilated Convolutions in Neural Networks for Left Atrial Segmentation in 3D Gadolinium Enhanced-MRI." Proceedings of the International Workshop on Statistical Atlases and Computational Models of the Heart Springer Verlag, 2019. 319-328.
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