ArthroSLAM: Multi-Sensor Robust Visual Localization for Minimally Invasive Orthopedic Surgery

Marmol, Andres; Corke, Peter; Peynot, Thierry

doi:10.1109/iros.2018.8593501

Cited by 11 publications

(8 citation statements)

References 14 publications

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“…Regarding TP evaluation, our results indicate that CLAHS, LLAP and the proposed LLSH degrade TP of SIFT but significantly improve TP of SURF (87%,93% -LLSH). Importantly, it was shown that SIFT is unreliable in tracking long arthroscopic sequences [27] and also our results show improvement, TP using SIFT remains insufficient on such long datasets. All of the enhancement methods improve SURF from very poor to extraordinary TP, particularly when using the LLSH.…”

Section: B Feature Detection and Matching Performancementioning

confidence: 56%

“…Results showed that SIFT features could be best extracted and matched (compared to SURF and others) in knee-arthroscopy, but the study used only sequences containing six unrealistic images not representative of the complexity and length of the procedure. In later work, SIFT features proved to be insufficient for tracking due to the dynamic character (occlusions, blur, glare, deformation) of the environment [27]. To overcome that challenge, sensor-fusion using arthroscopic images, external camera and robot's odometry was employed to provide robust localisation for knee arthroscopy [27], [28].…”

Section: Surgical Visionmentioning

confidence: 99%

“…In later work, SIFT features proved to be insufficient for tracking due to the dynamic character (occlusions, blur, glare, deformation) of the environment [27]. To overcome that challenge, sensor-fusion using arthroscopic images, external camera and robot's odometry was employed to provide robust localisation for knee arthroscopy [27], [28]. Essentially, the non-visual sensory information enabled a dense feature mapping thanks to the underpinned localisation improvements.…”

Section: Surgical Visionmentioning

confidence: 99%

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Saliency Improvement in Feature-Poor Surgical Environments Using Local Laplacian of Specified Histograms

et al. 2020

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Navigation in endoscopic environments requires an accurate and robust localisation system. A key challenge in such environments is the paucity of visual features that hinders accurate tracking. This paper examines the performance of three image enhancement techniques for tracking under such feature-poor conditions including Contrast Limited Adaptive Histogram Specification (CLAHS), Fast Local Laplacian Filtering (LLAP) and a new combination of the two coined Local Laplacian of Specified Histograms (LLSH). Two cadaveric knee arthroscopic datasets and an underwater seabed inspection dataset are used for the analysis, where results are interpreted by defining visual saliency as the number of correctly matched key-point (SIFT and SURF) features. Experimental results show a significant improvement in contrast quality and feature matching performance when image enhancement techniques are used. Results also demonstrate the LLSHs ability to vastly improve SURF tracking performance indicating more than 87% of successfully matched frames. A comparative analysis provides some important insights useful in the design of vision-based navigation for autonomous agents in feature-poor environments.

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Section: B Feature Detection and Matching Performancementioning

confidence: 56%

Section: Surgical Visionmentioning

confidence: 99%

Section: Surgical Visionmentioning

confidence: 99%

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Saliency Improvement in Feature-Poor Surgical Environments Using Local Laplacian of Specified Histograms

et al. 2020

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“…In recent years, our research group at the Queensland University of Technology (QUT) has made significant progress to circumvent these limitations [17]- [19]. Efforts to use Simultaneous Localization and Mapping (SLAM) have recently been applied to arthroscopy, but the extraction of key landmark features from images still remains an open challenge.…”

Section: Introductionmentioning

confidence: 99%

“…Using a cross validation experiment, the mean Dice coefficients for Femur, Tibia, ACL, and Meniscus are 0.78, 0.50, 0.41, 0.43 using the U-net and 0.79, 0.50, 0.51, 0.48 using the U-net++. This method represents the first step to improve our previously proposed medical robotic SLAM and depth mapping methods [17], [18].…”

Section: Introductionmentioning

confidence: 99%

Automatic Segmentation of Multiple Structures in Knee Arthroscopy Using Deep Learning

et al. 2020

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Minimally invasive surgery (MIS) is among the preferred procedures for treating a number of ailments as patients benefit from fast recovery and reduced blood loss. The trade-off is that surgeons lose direct visual contact with the surgical site and have limited intra-operative imaging techniques for real-time feedback. Computer vision methods as well as segmentation and tracking of the tissues and tools in the video frames, are increasingly being adopted to MIS to alleviate such limitations. So far, most of the advances in MIS have been focused on laparoscopic applications, with scarce literature on knee arthroscopy. Here for the first time, we propose a new method for the automatic segmentation of multiple tissue structures for knee arthroscopy. The training data of 3868 images were collected from 4 cadaver experiments, 5 knees, and manually contoured by two clinicians into four classes: Femur, Anterior Cruciate Ligament (ACL), Tibia, and Meniscus. Our approach adapts the U-net and the U-net++ architectures for this segmentation task. Using the cross-validation experiment, the mean Dice similarity coefficients for Femur, Tibia, ACL, and Meniscus are 0.78, 0.50, 0.41, 0.43 using the U-net and 0.79, 0.50, 0.51, 0.48 using the U-net++. While the reported segmentation method is of great applicability in terms of contextual awareness for the surgical team, it can also be used for medical robotic applications such as SLAM and depth mapping.

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Toward Bionic Arthroscopy: A Comprehensive Perspective of Continuum Robotics Principles and Prototypes for the Inception of Arthroscopic Surgery Robots

Huang,

Shi,

Gao

et al. 2023

Advanced Intelligent Systems

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In this study, an innovative exploration of leveraging bionics and continuum robotics principles to develop a novel solution for arthroscopic surgery is embarked on. Inspired by the flexibility and adaptability of organisms like snakes and octopuses, the continuum robot concept aims to address the inherent challenges in traditional arthroscopy, including lower precision, manual tremors, and long surgeon learning curves. The implementation of these principles in the human body, however, faces significant obstacles, particularly achieving high‐performance motion control amid strong nonlinearity and coupling between modules. This research focuses on intelligent integration and enhanced safety in human‐machine interaction, aiming for improved control precision and flexibility in arthroscopic procedures. A thorough literature review of endoscopic continuum robots is conducted, highlighting current advancements in actuation, structure, sensing, and control technologies. The study concludes with an assessment of these technologies, their limitations, and future potential, in light of the unique demands of arthroscopic continuum robots. This comprehensive review bridges bionics and robotics, presenting the opportunities and challenges in applying continuum robotics to arthroscopic surgery. The goal is to encourage further research in this area, contributing to the development of prototype robots that enhance the precision and safety of arthroscopic surgery.

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ArthroSLAM: Multi-Sensor Robust Visual Localization for Minimally Invasive Orthopedic Surgery

Cited by 11 publications

References 14 publications

Saliency Improvement in Feature-Poor Surgical Environments Using Local Laplacian of Specified Histograms

Saliency Improvement in Feature-Poor Surgical Environments Using Local Laplacian of Specified Histograms

Automatic Segmentation of Multiple Structures in Knee Arthroscopy Using Deep Learning

Toward Bionic Arthroscopy: A Comprehensive Perspective of Continuum Robotics Principles and Prototypes for the Inception of Arthroscopic Surgery Robots

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