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Purpose <p>This study proposes a generalization of markerless patient registration in image-guided neurosurgery based on depth information. The work builds on previous research to evaluate the performance of a range of commercial depth cameras and two different registration algorithms in this context.</p> Methods <p>A multimodal experimental setup was used, testing five depth cameras in seven configurations. Fiducial registration error (FRE) and target registration error (TRE) metrics were calculated using iterative closest point (ICP) and deep global registration (DGR) algorithms. A phantom head model was used to simulate clinical conditions, with cameras positioned to capture the face and craniotomy regions.</p> Results <p>The best-performing cameras, such as the D405 and Zed-M+, achieved TRE values as low as 2.36 ± 0.46 mm and 2.49 ± 0.35 mm, respectively, compared to manual registration that obtains a 1.37 mm error. Cameras equipped with texture projectors or enhanced depth refinement demonstrated improved performance. The proposed methodology effectively characterized the suitability of the camera for the registration tasks.</p> Conclusion <p>This study validates an adaptable and reproducible framework to evaluate depth cameras in neurosurgical scenarios, highlighting D405 and Zed-M + as reliable options. Future work will focus on improving depth quality through hardware and algorithmic improvements. The experimental data and the accompanying code were made publicly available to ensure reproducibility.</p>

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Benchmarking commercial depth sensors for intraoperative markerless registration in neurosurgery applications

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月23日

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  • Manuel Villa,
  • Jaime Sancho,
  • Gonzalo Rosa-Olmeda,
  • Miguel Chavarrias,
  • Eduardo Juarez,
  • Cesar Sanz

摘要

Purpose

This study proposes a generalization of markerless patient registration in image-guided neurosurgery based on depth information. The work builds on previous research to evaluate the performance of a range of commercial depth cameras and two different registration algorithms in this context.

Methods

A multimodal experimental setup was used, testing five depth cameras in seven configurations. Fiducial registration error (FRE) and target registration error (TRE) metrics were calculated using iterative closest point (ICP) and deep global registration (DGR) algorithms. A phantom head model was used to simulate clinical conditions, with cameras positioned to capture the face and craniotomy regions.

Results

The best-performing cameras, such as the D405 and Zed-M+, achieved TRE values as low as 2.36 ± 0.46 mm and 2.49 ± 0.35 mm, respectively, compared to manual registration that obtains a 1.37 mm error. Cameras equipped with texture projectors or enhanced depth refinement demonstrated improved performance. The proposed methodology effectively characterized the suitability of the camera for the registration tasks.

Conclusion

This study validates an adaptable and reproducible framework to evaluate depth cameras in neurosurgical scenarios, highlighting D405 and Zed-M + as reliable options. Future work will focus on improving depth quality through hardware and algorithmic improvements. The experimental data and the accompanying code were made publicly available to ensure reproducibility.