Automatic Segmentation of the Left Atrium from Computed Tomography Angiography Images
Author:
Publisher
Springer Science and Business Media LLC
Subject
Biomedical Engineering
Link
https://link.springer.com/content/pdf/10.1007/s10439-023-03170-9.pdf
Reference10 articles.
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3. Çiçek, Ö., A. Abdulkadir, S. S. Lienkamp, T. Brox, and Q. Ronneberger. 3D U-Net: learning dense volumetric segmentation from sparse annotation. In MICCAI. Cham: Springer, 2016, pp. 424–432.
4. Gonzales, R. A., F. Seemann, J. Lamy, et al. Automated left atrial time-resolved segmentation in MRI long-axis cine images using active contours. BMC Med. Imaging. 21:101, 2021.
5. Jin, C., et al. Left atrial appendage segmentation using fully convolutional neural networks and modified three-dimensional conditional random fields. IEEE J. Biomed. Health Inf. 22(6):1906–1916, 2018.
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