368-01-001 ISMRM Abstract

SGDR-Net: A Self-Guide Deformable Registration Network in DCE-MRI breast imaging

Primary: Analysis Methods - Image Registration and Alignment

Secondary: Body - Breast

368-01-001 · Registration, Atlases, and Motion · Monday, 11 May, 8:20 AM–9:15 AM · Digital Posters Row I

Keywords: Breast Imaging DCE-MRI Deep learning image registration Self-Guide Deformable Registration Network Plug-and-play

Accepted

Yichao Zhou¹, Shihui Zhou¹, Bing Han¹, LIJUN ZHANG¹

¹Research and Development Center, Canon Medical Systems (China), Beijing, China

Presenting Author: Mitsuhiro Bekku

Synopsis

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References

1. K. Kuczynski, M. Siczek, and R. Stegierski, “Dce-mri breast image registration for tumour diagnostics,” in Image Processing and Communications Challenges 3, R. S. Chora´s, Ed. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011, pp. 323–329.

2. C. Mattusch, U. Bick, and F. Michallek, “Development and validation of a four-dimensional registration technique for dce breast mri,” Insights into Imaging, vol. 14, no. 1, p. 17, 2023. https://doi.org/10.1186/s13244-022-01362-w [doi]

3. G. Balakrishnan, A. Zhao, M. R. Sabuncu, A. V. Dalca and J. Guttag, "An Unsupervised Learning Model for Deformable Medical Image Registration," 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA, 2018, pp. 9252-9260, doi: 10.1109/CVPR.2018.00964. [doi]

4. J. Chen, E. C. Frey, Y. He, W. P. Segars, Y. Li, and Y. Du, “Transmorph: Transformer for unsupervised medical image registration,” Medical Image Analysis, vol. 82, p. 102615, 2022. https://doi.org/10.1016/j.media.2022.102615. [doi]

5. M. Jaderberg, K. Simonyan, A. Zisserman, and K. Kavukcuoglu, “Spatial transformer networks,” Neural Information Processing Systems, Neural Information Processing Systems, Dec 2015.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/368-01-001