Video-Based Computer Aided Arthroscopy for Patient Specific Reconstruction of the Anterior Cruciate Ligament

Raposo, Carolina; Sousa, Cátia; Ribeiro, Luís; Melo, Ricardo; Barreto, João Pedro; Oliveira, JP; Pinto-Marques, Pedro; Fonseca, Fernando

doi:10.1007/978-3-030-00937-3_15

Cited by 6 publications

(10 citation statements)

References 16 publications

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“…Video-based computer-aided TKA: This section overviews the proposed concept for computer-aided TKA that uses a navigation sensor to perform 3D pose estimation during the open surgery. By using additional depth-sensing capabilities, we avoid the use of visual markers as in [6] (see Fig. 1).…”

mentioning

confidence: 99%

Deep segmentation leverages geometric pose estimation in computer‐aided total knee arthroplasty

Rodrigues

Antunes

Raposo

et al. 2019

Healthcare Technology Letters

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Knee arthritis is a common joint disease that usually requires a total knee arthroplasty. There are multiple surgical variables that have a direct impact on the correct positioning of the implants, and an optimal combination of all these variables is the most challenging aspect of the procedure. Usually, preoperative planning using a computed tomography scan or magnetic resonance imaging helps the surgeon in deciding the most suitable resections to be made. This work is a proof of concept for a navigation system that supports the surgeon in following a preoperative plan. Existing solutions require costly sensors and special markers, fixed to the bones using additional incisions, which can interfere with the normal surgical flow. In contrast, the authors propose a computer-aided system that uses consumer RGB and depth cameras and do not require additional markers or tools to be tracked. They combine a deep learning approach for segmenting the bone surface with a recent registration algorithm for computing the pose of the navigation sensor with respect to the preoperative 3D model. Experimental validation using ex-vivo data shows that the method enables contactless pose estimation of the navigation sensor with the preoperative model, providing valuable information for guiding the surgeon during the medical procedure.

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mentioning

confidence: 99%

Deep segmentation leverages geometric pose estimation in computer‐aided total knee arthroplasty

Rodrigues

Antunes

Raposo

et al. 2019

Healthcare Technology Letters

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“…The multi-variate Gaussian distribution is utilized to model the localization error associated with the positional vectors. With the above (modified vMF) distribution presented in (1), the probability of one generalized intraoperative data point…”

Section: B Rigid Point Set Registration With Normal and Tangent Vectorsmentioning

confidence: 99%

“…medical condition [1]. ACL reconstruction is the procedure where the torn ligament is replaced by a tissue graft, which is pulled into the knee joint through tunnels [1], [2].…”

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confidence: 99%

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Aligning 3D Curve With Surface Using Tangent and Normal Vectors for Computer-Assisted Orthopedic Surgery

Min

Zhu

Liu

et al. 2021

IEEE Trans. Med. Robot. Bionics

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Registration that aligns different views of one interested organ together is an essential technique and outstanding problem in medical robotics and image-guided surgery (IGS). This work introduces a novel rigid point set registration (PSR) approach that aims to accurately map the pre-operative space with the intra-operative space to enable successful image guidance for computer-assisted orthopaedic surgery (CAOS). The normal vectors and tangent vectors are first extracted from the pre-operative and intra-operative point sets (PSs) respectively, and are further utilized to enhance the registration accuracy and robustness. The contributions of this article are threefolds. First, we propose and formulate a novel distribution that describes the error between one normal vector and the corresponding tangent vector based on the von-Mises Fisher (vMF) distribution. Second, by modelling the anisotropic position localization error with the multi-variate Gaussian distribution, we formulate the PSR considering anisotropic localization error as a maximum likelihood estimation (MLE) problem and then solve it under the expectation maximization (EM) framework. Third, to facilitate the optimization process, the gradients of the objective function with respect to the desired parameters are computed and presented. Extensive experimental results on the human femur and pelvis models verify that the proposed approach outperforms the state-of-the-art methods, and demonstrate potential clinical values for relevant surgical navigation applications.

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“…Contrary to optical tracking [ 8 ], video-based surgical navigation (VBSN) [ 16 ] leverages visual markers attached to the patient’s anatomy to guide the surgeon throughout the medical procedure. When applied to arthroscopic procedures, these markers are placed inside the joint, precluding the need for additional external incisions.…”

Section: Introductionmentioning

confidence: 99%

Structured light for touchless 3D registration in video-based surgical navigation

Baptista,

Marques,

Raposo

et al. 2024

Int J CARS

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Purpose Arthroscopic surgery, with its inherent difficulties on visibility and maneuverability inside the joint, poses significant challenges to surgeons. Video-based surgical navigation (VBSN) has proven to have clinical benefits in arthroscopy but relies on a time-consuming and challenging surface digitization using a touch probe to accomplish registration of intraoperative data with preoperative anatomical models. This paper presents an off-the-shelf laser scanner for noninvasive registration that enables an increased area of reachable region. Methods Our solution uses a standard arthroscope and a light projector with visual markers for real-time extrinsic calibration. Nevertheless, the shift from a touch probe to a laser scanner introduces a new challenge—the presence of a significant amount of outliers resulting from the reconstruction of nonrigid structures. To address this issue, we propose to identify the structures of interest prior to reconstruction using a deep learning-based semantic segmentation technique. Results Experimental validation using knee and hip phantoms, as well as ex-vivo data, assesses the laser scanner’s effectiveness. The integration of the segmentation model improves results in ex-vivo experiments by mitigating outliers. Specifically, the laser scanner with the segmentation model achieves registration errors below 2.2 mm, with the intercondylar region exhibiting errors below 1 mm. In experiments with phantoms, the errors are always below 1 mm. Conclusion The results show the viability of integrating the laser scanner with VBSN as a noninvasive and potential alternative to traditional methods by overcoming surface digitization challenges and expanding the reachable region. Future efforts aim to improve hardware to further optimize performance and applicability in complex procedures.

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Video-Based Computer Aided Arthroscopy for Patient Specific Reconstruction of the Anterior Cruciate Ligament

Cited by 6 publications

References 16 publications

Deep segmentation leverages geometric pose estimation in computer‐aided total knee arthroplasty

Deep segmentation leverages geometric pose estimation in computer‐aided total knee arthroplasty

Aligning 3D Curve With Surface Using Tangent and Normal Vectors for Computer-Assisted Orthopedic Surgery

Structured light for touchless 3D registration in video-based surgical navigation

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