3D augmentation for volumetric whole heart segmentation
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Publisher
Springer Science and Business Media LLC
Link
https://www.nature.com/articles/s41598-024-72469-x.pdf
Reference14 articles.
1. Zhuang, X. et al. Evaluation of algorithms for multi-modality whole heart segmentation: An open-access grand challenge. Med. Image Anal. 58, 101537. https://doi.org/10.1016/j.media.2019.101537 (2019).
2. Xiong, Z. et al. A global benchmark of algorithms for segmenting the left atrium from late gadolinium-enhanced cardiac magnetic resonance imaging. Med. Image Anal. 67, 101832. https://doi.org/10.1016/j.media.2020.101832 (2021).
3. Xia, Q., Yao, Y., Hu, Z. & Hao, A. Automatic 3D atrial segmentation from GE-MRIs using volumetric fully convolutional networks. In Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges. Vol. 11395. 211–220.https://doi.org/10.1007/978-3-030-12029-0_23 (Springer, 2019).
4. Ronneberger, O., Fischer, P. & Brox, T. U-Net: Convolutional Networks for Biomedical Image Segmentation (2015).
5. Çiçek, O., Abdulkadir, A., Lienkamp, S. S., Brox, T. & Ronneberger, O. 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation (2016).
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