Enhancing spatial perception in robot-assisted minimally invasive surgery with edge-preserving depth estimation and pose tracking.

Bo Guan, Jianchang Zhao, Bo Yi, Jianmin Li

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Article in BMC surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

4 authors.

Bo GuanKey Lab for Mechanism Theory and Equipment Design of Ministry of Education, Tianjin University, Tianjin, 300072, China.

Jianchang ZhaoNational Engineering Research Center of Neuromodulation, Tsinghua University, Beijing, 100084, China.

Bo YiDepartment of General Surgery, Third Xiangya Hospital, Central South University, Changsha, 410013, China.

Jianmin LiKey Lab for Mechanism Theory and Equipment Design of Ministry of Education, Tianjin University, Tianjin, 300072, China. mjli@tju.edu.cn.

Funding

National Natural Science Foundation of China 52525504Tianjin Excellent Young Scientists Fund Program 24JCJQJC00090

6 · The paper itself

Abstract

backgroundEnhancing the safety of robot-assisted minimally invasive surgery (RAMIS) is critically dependent on improving the robot's spatial understanding of the surgical scene. However, the quality of laparoscopic images is often negatively affected by factors such as uneven lighting, blurred textures, and occlusions, all of which can interfere with the accurate acquisition of depth information.

methodsTo address these challenges, we develop a depth estimation and pose tracking method that incorporates a dual-stream Transformer stereo matching network and a vision-based tracking technique.

resultsExperimental results indicate that the proposed method can effectively maintain the boundary information of anatomical structures and demonstrate better performance in the robustness of laparoscope pose tracking.

conclusionsThis paper presents a robotic-assisted minimally invasive surgery navigation framework that achieves accurate scene depth estimation and pose tracking, thereby enhancing the robot's spatial understanding of the surgical environment.

Indexed as

Depth PerceptionLaparoscopyMinimally Invasive Surgical ProceduresRobotic Surgical ProceduresSpace PerceptionHumansDepth estimationLaparoscope trackingMinimally invasive surgeryTissue reconstruction

Identifiers

PMID41053721

DOI10.1186/s12893-025-03198-9

PMCPMC12502414

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LicenceCC BY-NC-ND

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.