DeepRecon: Joint 2D Cardiac Segmentation and 3D Volume Reconstruction via a Structure-Specific Generative Method-Reference-Cited by-同舟云学术

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DeepRecon: Joint 2D Cardiac Segmentation and 3D Volume Reconstruction via a Structure-Specific Generative Method

Published:2022 Issue: Volume: Page:567-577
ISSN:0302-9743
Container-title:Lecture Notes in Computer Science
language:
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Author:

Chang Qi,Yan Zhennan,Zhou Mu,Liu Di,Sawalha Khalid,Ye Meng,Zhangli Qilong,Kanski Mikael,Al’Aref Subhi,Axel Leon,Metaxas Dimitris

Publisher

Springer Nature Switzerland

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-16440-8_54

Reference29 articles.

1. Avants, B.B., Tustison, N.J., Song, G., Cook, P.A., Klein, A., Gee, J.C.: A reproducible evaluation of ants similarity metric performance in brain image registration. Neuroimage 54(3), 2033–2044 (2011)

2. Awori, J., et al.: 3D models improve understanding of congenital heart disease. 3D Print. Med. 7(1), 1–9 (2021)

3. Biffi, C., et al.: 3D high-resolution cardiac segmentation reconstruction from 2D views using conditional variational autoencoders. In: 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), pp. 1643–1646. IEEE (2019)

4. Campello, V.M., et al.: Multi-centre, multi-vendor and multi-disease cardiac segmentation: the M &MS challenge. IEEE Trans. Med. Imaging 40(12), 3543–3554 (2021)

5. Chang, Q., Yan, Z., Lou, Y., Axel, L., Metaxas, D.N.: Soft-label guided semi-supervised learning for bi-ventricle segmentation in cardiac cine MRI. In: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), pp. 1752–1755. IEEE (2020)

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