Recursive Cascaded Networks for Unsupervised Medical Image Registration

Abstract: We present recursive cascaded networks, a general architecture that enables learning deep cascades, for deformable image registration. The proposed architecture is simple in design and can be built on any base network. The moving image is warped successively by each cascade and finally aligned to the fixed image; this procedure is recursive in a way that every cascade learns to perform a progressive deformation for the current warped image. The entire system is end-to-end and jointly trained in an unsupervised manner. In addition, enabled by the recursive architecture, one cascade can be iteratively applied for multiple times during testing, which approaches a better fit between each of the image pairs. We evaluate our method on 3D medical images, where deformable registration is most commonly applied. We demonstrate that recursive cascaded networks achieve consistent, significant gains and outperform state-of-the-art methods. The performance reveals an increasing trend as long as more cascades are trained, while the limit is not observed. Code is available at https://github.com/microsoft/Recursive-Cascaded-Networks.

• The paper presents a novel recursive cascaded network architecture that refines registration iteratively without supervision.
• It leverages multi-level feature representations to capture both global and local deformations in medical images.
• Experimental results demonstrate enhanced registration accuracy compared to conventional methods, highlighting its practical impact.

Overview of "LaTeX Author Guidelines for ICCV Proceedings"

The document in question provides detailed guidelines for the preparation and submission of manuscripts intended for the International Conference on Computer Vision (ICCV) proceedings. As this serves as an instructional guide rather than a research study, it occupies a critical role in ensuring uniformity and quality across all submissions. These guidelines can aid authors in navigating the intricacies of formatting, styling, and procedural specifications required by ICCV.

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Conclusion

This document is instrumental in defining the standards for manuscript submissions for ICCV and similar academic conferences. By providing a robust framework, it ensures the integrity and accessibility of scientific discourse. These guidelines are a testament to the methodical preparation required for academic publishing, reinforcing practices that uphold the quality and clarity of scholarly communications in the field of computer vision and beyond.