Multispectral Image Analysis for Patient Tissue Tracking During Complex Interventions

Manni, Francesca; Mamprin, Marco; Zinger, Svitlana; Shan, Caifeng; Holthuizen, Ronald

doi:10.1109/icip.2018.8451263

Cited by 6 publications

(7 citation statements)

References 12 publications

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“…In this application, they allow the assessing of the 3D location of the spine and eventually, motion correction. We have previously adapted SURF and MSER to study the feasibility of anatomical landmark detection on skin or spinal column [ 31 , 38 ].…”

Section: Methodsmentioning

confidence: 99%

Towards Optical Imaging for Spine Tracking without Markers in Navigated Spine Surgery

Manni

Elmi-Terander

Burström

et al. 2020

Sensors

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Surgical navigation systems are increasingly used for complex spine procedures to avoid neurovascular injuries and minimize the risk for reoperations. Accurate patient tracking is one of the prerequisites for optimal motion compensation and navigation. Most current optical tracking systems use dynamic reference frames (DRFs) attached to the spine, for patient movement tracking. However, the spine itself is subject to intrinsic movements which can impact the accuracy of the navigation system. In this study, we aimed to detect the actual patient spine features in different image views captured by optical cameras, in an augmented reality surgical navigation (ARSN) system. Using optical images from open spinal surgery cases, acquired by two gray-scale cameras, spinal landmarks were identified and matched in different camera views. A computer vision framework was created for preprocessing of the spine images, detecting and matching local invariant image regions. We compared four feature detection algorithms, Speeded Up Robust Feature (SURF), Maximal Stable Extremal Region (MSER), Features from Accelerated Segment Test (FAST), and Oriented FAST and Rotated BRIEF (ORB) to elucidate the best approach. The framework was validated in 23 patients and the 3D triangulation error of the matched features was < 0.5 mm. Thus, the findings indicate that spine feature detection can be used for accurate tracking in navigated surgery.

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Section: Methodsmentioning

confidence: 99%

Towards Optical Imaging for Spine Tracking without Markers in Navigated Spine Surgery

Manni

Elmi-Terander

Burström

et al. 2020

Sensors

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“…It depends on the characteristics of feature on the image themselves. Images containing 'round feature shape' like vegetation, shrubbery area and etc were suited with 'blob' detectors (SIFT and SURF) since it more efficient on those conditions [54] [55] and [56]. SURF detectors performed efficiently in multispectral face recognitions where the combinations of conventional SURF detectors and FREAK descriptions extracted highest number of feature matching compare others algorithms [57].…”

Section: Multispectral Image Matchingmentioning

confidence: 99%

Multispectral Image Matching Using SIFT and SURF Algorithm: A Review

2023

IJG

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SIFT and SURF image matching were used in many industries such as survey and mapping, geology, medical and automotive. Multispectral sensors offered today become new challenge for researchers to study the performances of SIFT and SURF algorithms on multispectral image. Basically, multispectral image consists of more than three bands. As a result, the differences between those bands leads to nonlinear intensity between images. Both algorithm detectors using ‘blob detector’ that extracting the feature points as a key point for image matching later on. Hence, the less visibility of the feature on the multispectral images was one of the issues need to be solved. Many researchers investigate and propose a new strategy to extract and match the feature point using SIFT and SURF on multispectral image. The image fusions, combinations of different descriptors and revised or alteration of the algorithm themselves were among the approached taken by researchers in order to achieved good results.

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“…We have previously presented a novel approach for tissue tracking by using multispectral imaging (MSI). We have acquired multispectral (MS) images from the skin of healthy volunteers with an MS camera, which uses a filter wheel and multiple light sources [25]. We have proved the feasibility of tracking skin features without any markers, reaching sub-pixel accuracy for wavelengths at 430 nm and 970 nm, when a simulated movement transformation is applied to the MSI images.…”

Section: Introductionmentioning

confidence: 98%

Hyperspectral Imaging for Skin Feature Detection: Advances in Markerless Tracking for Spine Surgery

et al. 2020

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In spinal surgery, surgical navigation is an essential tool for safe intervention, including the placement of pedicle screws without injury to nerves and blood vessels. Commercially available systems typically rely on the tracking of a dynamic reference frame attached to the spine of the patient. However, the reference frame can be dislodged or obscured during the surgical procedure, resulting in loss of navigation. Hyperspectral imaging (HSI) captures a large number of spectral information bands across the electromagnetic spectrum, providing image information unseen by the human eye. We aim to exploit HSI to detect skin features in a novel methodology to track patient position in navigated spinal surgery. In our approach, we adopt two local feature detection methods, namely a conventional handcrafted local feature and a deep learning-based feature detection method, which are compared to estimate the feature displacement between different frames due to motion. To demonstrate the ability of the system in tracking skin features, we acquire hyperspectral images of the skin of 17 healthy volunteers. Deep-learned skin features are detected and localized with an average error of only 0.25 mm, outperforming the handcrafted local features with respect to the ground truth based on the use of optical markers.

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Multispectral Image Analysis for Patient Tissue Tracking During Complex Interventions

Cited by 6 publications

References 12 publications

Towards Optical Imaging for Spine Tracking without Markers in Navigated Spine Surgery

Towards Optical Imaging for Spine Tracking without Markers in Navigated Spine Surgery

Multispectral Image Matching Using SIFT and SURF Algorithm: A Review

Hyperspectral Imaging for Skin Feature Detection: Advances in Markerless Tracking for Spine Surgery

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