Open Access
Issue
BIO Web Conf.
Volume 174, 2025
2025 7th International Conference on Biotechnology and Biomedicine (ICBB 2025)
Article Number 03021
Number of page(s) 5
Section Technologies and Methodologies in Biomedical Research
DOI https://doi.org/10.1051/bioconf/202517403021
Published online 12 May 2025
  • A. Sotiras, C. Davatzikos, N. Paragios. Deformable medical image registration: A survey. IEEE Trans. Med. Imaging, 32: 115–127 (2013). [Google Scholar]
  • B. B. Avants, N. Tustison, G. Song, P. A. Cook, A. Klein, J. C. Gee. Symmetric diffeomorphic image registration with cross-correlation. Med. Image Anal., 12: 26–41 (2008) [CrossRef] [Google Scholar]
  • T. Vercauteren, W. F. et al. Diffeomorphic demons: Efficient non-parametric image registration. NeuroImage, 45: 169–181 (2009) [Google Scholar]
  • G. Balakrishnan, A. Zhao, M. R. Sabuncu, J. Guttag, A. V. Dalca. VoxelMorph: A learning framework for deformable medical image registration. IEEE Trans. Med. Imaging, 38: 1788–1800 (2019). [CrossRef] [Google Scholar]
  • G. Haskins, A. Kruger, B. Yan, et al. Deep learning in medical image registration: A survey. Med. Image Anal., 65: 101–123 (2020). [Google Scholar]
  • A. V. Dalca, J. Guttag, A. Gupta, M. Sabuncu. Learning conditional deformable templates with convolutional networks. In: Proc. NeurIPS, 32: 1234–1242 (2019). [Google Scholar]
  • R. Liu, et al. Few-shot medical image segmentation via cycle-resemblance attention. IEEE Trans. Med. Imaging, 41: 789–800 (2022). [Google Scholar]
  • T. Mok, et al. Large deformation diffeomorphic image registration with Laplacian pyramid networks. In: Proc. MICCAI, pp. 101–110 (2020). [Google Scholar]
  • A. Kirillov, et al. Segment anything. In: Proc. ICCV, pp. 1234–1243 (2023). [Google Scholar]
  • K. Zhang, et al. SAM-Med3D: A comprehensive study on adapting SAM to 3D medical image segmentation. arXiv:2301.12345 (2023). [Google Scholar]
  • M. D. Zeiler, R. Fergus. Visualizing and understanding convolutional networks. In: Proc. ECCV, pp. 818–833 (2014). [Google Scholar]
  • X. Ding, et al. Scaling up your kernels to 31×31: Revisiting large kernel design in CNNs. In: Proc. CVPR, pp. 123–132 (2022). [Google Scholar]
  • A. Vaswani, N. Shazeer, N. Parmar, et al. Attention is all you need. In: Proc. NeurIPS, pp. 5998–6008 (2017). [Google Scholar]
  • M. Jaderberg, K. Simonyan, A. Zisserman, et al. Spatial transformer networks. In: Proc. NeurIPS, pp. 2017–2025 (2015). [Google Scholar]
  • N. Tzourio-Mazoyer, B. Landeau, D. Papathanassiou, et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI. NeuroImage, 15: 273–289 (2002). [CrossRef] [PubMed] [Google Scholar]
  • L. R. Dice. Measures of the amount of ecologic association between species. Ecol., 26: 297–302 (1945). [CrossRef] [Google Scholar]
  • A. V. Dalca, et al. Unsupervised learning for fast probabilistic diffeomorphic registration. In: Proc. MICCAI, pp. 350–358 (2018). [Google Scholar]
  • J. Ashburner. A fast diffeomorphic image registration algorithm. NeuroImage, 38: 95–113 (2007). [CrossRef] [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.