# An Optimization-based Baseline for Rigid 2D/3D Registration Applied to Spine Surgical Navigation Using CMA-ES

arXiv: https://arxiv.org/abs/2402.05642 · HTML: https://mlsanity.com/paper/2402.05642/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 20 references that are in the map.
- First posted on arXiv 8 Feb 2024.

## Builds on (most cited first, 10 of 10)

- [Deep Learning in Medical Image Registration: A Review](https://mlsanity.com/paper/1912.12318/) (2019)
- [Fast Auto-Differentiable Digitally Reconstructed Radiographs for Solving Inverse Problems in Intraoperative Imaging](https://mlsanity.com/paper/2208.12737/) (2022)
- [Intraoperative 2D/3D Image Registration via Differentiable X-ray Rendering](https://mlsanity.com/paper/2312.06358/) (2023)
- [Dilated FCN for Multi-Agent 2D/3D Medical Image Registration](https://mlsanity.com/paper/1712.01651/) (2017)
- [The Impact of Machine Learning on 2D/3D Registration for Image-guided Interventions: A Systematic Review and Perspective](https://mlsanity.com/paper/2108.02238/) (2021)
- [Generalizing Spatial Transformers to Projective Geometry with Applications to 2D/3D Registration](https://mlsanity.com/paper/2003.10987/) (2020)
- [Patch-Based Image Similarity for Intraoperative 2D/3D Pelvis Registration During Periacetabular Osteotomy](https://mlsanity.com/paper/1909.10443/) (2019)
- [Embedded Feature Similarity Optimization with Specific Parameter Initialization for 2D/3D Medical Image Registration](https://mlsanity.com/paper/2305.06252/) (2023)
- [SpineCLUE: Automatic Vertebrae Identification Using Contrastive Learning and Uncertainty Estimation](https://mlsanity.com/paper/2401.07271/) (2024)
- [Fully Differentiable Correlation-driven 2D/3D Registration for X-ray to CT Image Fusion](https://mlsanity.com/paper/2402.02498/) (2024)

## Built on by (most cited first, 4 of 4)

- [Rigid Single-Slice-in-Volume registration via rotation-equivariant 2D/3D feature matching](https://mlsanity.com/paper/2410.18683/) (2024)
- [Intraoperative 2D/3D Registration via Spherical Similarity Learning and Differentiable Levenberg-Marquardt Optimization](https://mlsanity.com/paper/2509.06890/) (2025)
- [Veriserum: A dual-plane fluoroscopic dataset with knee implant phantoms for deep learning in medical imaging](https://mlsanity.com/paper/2509.05483/) (2025)
- [Patient-Agnostic Synthetic Pretraining for Efficient Patient-Specific Intraoperative 2D/3D Registration](https://mlsanity.com/paper/2607.23343/) (2026)

## Abstract (arXiv)

A robust and efficient optimization-based 2D/3D registration framework is crucial for the navigation system of orthopedic surgical robots. It can provide precise position information of surgical instruments and implants during surgery. While artificial intelligence technology has advanced rapidly in recent years, traditional optimization-based registration methods remain indispensable in the field of 2D/3D registration.he exceptional precision of this method enables it to be considered as a post-processing step of the learning-based methods, thereby offering a reliable assurance for registration. In this paper, we present a coarse-to-fine registration framework based on the CMA-ES algorithm. We conducted intensive testing of our method using data from different parts of the spine. The results shows the effectiveness of the proposed framework on real orthopedic spine surgery clinical data. This work can be viewed as an additional extension that complements the optimization-based methods employed in our previous studies.