Comparative evaluation of three commercially available markerless depth sensors for close-range use in surgical simulation

Abstract

Purpose Minimally invasive surgeries have restricted surgical ports, demanding a high skill level from the surgeon. Surgical simulation potentially reduces this steep learning curve and additionally provides quantitative feedback. Markerless depth sensors show great promise for quantification, but most such sensors are not designed for accurate reconstruction of complex anatomical forms in close-range. Methods This work compares three commercially available depth sensors, namely the Intel D405, D415, and the Stereolabs Zed-Mini in the range of 12–20 cm, for use in surgical simulation. Three environments are designed that closely mimic surgical simulation, comprising planar surfaces, rigid objects, and mitral valve models of silicone and realistic porcine tissue. The cameras are evaluated on Z-accuracy, temporal noise, fill rate, checker distance, point cloud comparisons, and visual inspection of surgical scenes, across several camera settings. Results The Intel cameras show sub-mm accuracy in most static environments. The D415 fails in reconstructing valve models, while the Zed-Mini provides lesser temporal noise and higher fill rate. The D405 could reconstruct anatomical structures like the mitral valve leaflet and a ring prosthesis, but performs poorly for reflective surfaces like surgical tools and thin structures like sutures. Conclusion If a high temporal resolution is needed and lower spatial resolution is acceptable, the Zed-Mini is the best choice, whereas the Intel D405 is the most suited for close-range applications. The D405 shows potential for applications like deformable registration of surfaces, but is not yet suitable for applications like real-time tool tracking or surgical skill assessment.

Experimental Setup for Evaluating Depth Sensors in Augmented Reality Technologies Used in Medical Devices

“…Amid this rapid development and widespread adoption of AR technologies across various applications, evaluating these technologies to assess their reliability has received comparatively less attention. Most efforts have been directed toward evaluating these applications as a whole using qualitative methods [10][11][12][13], which rely heavily on subjective user feedback and focus on whether a Disclaimer/Publisher's Note: The statements, opinions, and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions, or products referred to in the content.…”

Experimental Setup for Evaluating Depth Sensors in Augmented Reality Technologies Used in Medical Devices

“…Amid this rapid development and widespread adoption of AR technologies across various applications, evaluating these technologies to assess their reliability has received comparatively less attention. Most efforts have been directed toward evaluating these applications as a whole using qualitative methods [ 13 , 14 , 15 , 16 ], which rely heavily on subjective user feedback and focus on whether a very specific task can be completed better or worse using applications reliant on depth sensors. For example, the review paper discusses various evaluations of gesture tracking enabled by depth cameras [ 17 ], and a different collection of works examines improvements in the performance of simultaneous localization and mapping (SLAM) algorithms when depth cameras are used [ 18 , 19 ].…”

Teachers should apply the principle of reduction for more sustainable surgical simulation practice: the example of training pharyngolaryngeal surgery in a porcine model

“…: heart ( 7 ), eyes ( 8 )], to train for complex procedures [e.g. : liver transplantation ( 9 ), craniostenosis treatment ( 10 ), endoscopic submucosal dissection ( 11 ), and to design ( 12 )] or to train for the use of medical devices mainly in the field of robotic surgery ( 10 , 13 , 14 ). Given 1/difficulty of accessing food resources around the world ( 15 ), and 2/the environmental impact of pig production ( 16 ), we need to question the relevance of using such models for surgical learning.…”