# Embedded Feature Similarity Optimization with Specific Parameter Initialization for 2D/3D Medical Image Registration

arXiv: https://arxiv.org/abs/2305.06252 · HTML: https://mlsanity.com/paper/2305.06252/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 33 references that are in the map.
- First posted on arXiv 10 May 2023.

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [ResNeSt: Split-Attention Networks](https://mlsanity.com/paper/2004.08955/) (2020)
- [geomstats: a Python Package for Riemannian Geometry in Machine Learning](https://mlsanity.com/paper/1805.08308/) (2018)
- [CBNet: A Novel Composite Backbone Network Architecture for Object Detection](https://mlsanity.com/paper/1909.03625/) (2019)
- [Biomechanics-informed Neural Networks for Myocardial Motion Tracking in MRI](https://mlsanity.com/paper/2006.04725/) (2020)
- [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)
- [Cross-modal Attention for MRI and Ultrasound Volume Registration](https://mlsanity.com/paper/2107.04548/) (2021)
- [Real-time 2D/3D Registration via CNN Regression](https://mlsanity.com/paper/1507.07505/) (2015)
- [End-to-end Ultrasound Frame to Volume Registration](https://mlsanity.com/paper/2107.06449/) (2021)
- [Vertebrae localization, segmentation and identification using a graph optimization and an anatomic consistency cycle](https://mlsanity.com/paper/2110.12177/) (2021)
- [Deep Iterative 2D/3D Registration](https://mlsanity.com/paper/2107.10004/) (2021)

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

- [An Optimization-based Baseline for Rigid 2D/3D Registration Applied to Spine Surgical Navigation Using CMA-ES](https://mlsanity.com/paper/2402.05642/) (2024)
- [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)
- [Patient-Agnostic Synthetic Pretraining for Efficient Patient-Specific Intraoperative 2D/3D Registration](https://mlsanity.com/paper/2607.23343/) (2026)

## Abstract (arXiv)

We present a novel deep learning-based framework: Embedded Feature Similarity Optimization with Specific Parameter Initialization (SOPI) for 2D/3D medical image registration which is a most challenging problem due to the difficulty such as dimensional mismatch, heavy computation load and lack of golden evaluation standard. The framework we design includes a parameter specification module to efficiently choose initialization pose parameter and a fine-registration module to align images. The proposed framework takes extracting multi-scale features into consideration using a novel composite connection encoder with special training techniques. We compare the method with both learning-based methods and optimization-based methods on a in-house CT/X-ray dataset as well as simulated data to further evaluate performance. Our experiments demonstrate that the method in this paper has improved the registration performance, and thereby outperforms the existing methods in terms of accuracy and running time. We also show the potential of the proposed method as an initial pose estimator. The code is available at https://github.com/m1nhengChen/SOPI