Stereo Endoscopic Camera Pose Optimal Estimation by Structure Similarity Index Measure Integration

IF 2.3 3区医学 Q2 SURGERY

International Journal of Medical Robotics and Computer Assisted Surgery Pub Date : 2025-05-25 DOI:10.1002/rcs.70078

Ruoqi Lian, Wei Li, Junchen Hao, Yanfang Zhang, Fucang Jia

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引用次数: 0

Abstract

Background

Accurate endoscopic camera pose estimation is crucial for real-time AR navigation systems. While current methods primarily use depth and optical flow, they often ignore structural inconsistencies between images.

Methods

Leveraging the RAFT framework, we process sequential stereo RGB pairs to extract optical flow and depth features for pose estimation. To address structural inconsistencies, we refine the weights for both 2D and 3D residuals by computing SSIM indices for the left and right views, as well as pre- and post-optical flow transformations. The SSIM metric is also used in the loss function.

Results

Experiments on the StereoMIS dataset demonstrate our method's improved pose estimation accuracy compared to rigid SLAM methods, showing a lower accumulated trajectory error (ATE-RMSE: 18.5 mm). Additionally, ablation experiments achieved an 11.49% reduction in average error.

Conclusion

The pose estimation accuracy has been improved by incorporating SSIM. The code is available at: https://github.com/lianrq/pose-estimation-by-SSIM-Integration.

查看原文本刊更多论文

基于结构相似度测度积分的立体内窥镜相机位姿优化估计

准确的内窥镜相机姿态估计是实时AR导航系统的关键。虽然目前的方法主要使用深度和光流，但它们经常忽略图像之间的结构不一致性。方法利用RAFT框架，对序列立体RGB对进行处理，提取用于姿态估计的光流和深度特征。为了解决结构不一致性，我们通过计算左视图和右视图的SSIM指数以及前后光流变换来细化2D和3D残差的权重。SSIM度量也用于损失函数。结果在StereoMIS数据集上的实验表明，与刚性SLAM方法相比，我们的方法提高了姿态估计精度，显示出更低的累积轨迹误差（late - rmse: 18.5 mm）。此外，烧蚀实验的平均误差降低了11.49%。结论结合SSIM提高了姿态估计的精度。代码可从https://github.com/lianrq/pose-estimation-by-SSIM-Integration获得。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Journal of Medical Robotics and Computer Assisted Surgery 医学-外科

CiteScore

4.50

自引率

12.00%

发文量

131

审稿时长

6-12 weeks

期刊介绍： The International Journal of Medical Robotics and Computer Assisted Surgery provides a cross-disciplinary platform for presenting the latest developments in robotics and computer assisted technologies for medical applications. The journal publishes cutting-edge papers and expert reviews, complemented by commentaries, correspondence and conference highlights that stimulate discussion and exchange of ideas. Areas of interest include robotic surgery aids and systems, operative planning tools, medical imaging and visualisation, simulation and navigation, virtual reality, intuitive command and control systems, haptics and sensor technologies. In addition to research and surgical planning studies, the journal welcomes papers detailing clinical trials and applications of computer-assisted workflows and robotic systems in neurosurgery, urology, paediatric, orthopaedic, craniofacial, cardiovascular, thoraco-abdominal, musculoskeletal and visceral surgery. Articles providing critical analysis of clinical trials, assessment of the benefits and risks of the application of these technologies, commenting on ease of use, or addressing surgical education and training issues are also encouraged. The journal aims to foster a community that encompasses medical practitioners, researchers, and engineers and computer scientists developing robotic systems and computational tools in academic and commercial environments, with the intention of promoting and developing these exciting areas of medical technology.