M. Rueff, Josh Glenboski, Roger Lacaille, L. Angibaud
Balancing soft tissues in the knee with the patella in place and with regularly applied force helps surgeons make decisions for positioning knee components in a manner that is friendly to soft tissues. A novel intraarticular device has been developed for achieving a balanced knee joint over the range of motion of the knee without requiring manual adjustments during surgery. Quasi-Constant force output was generated by the device at usual joint gaps for the knee sizes encountered during total knee arthroplasty.
{"title":"Intraarticular Quasi-Constant Force Tension in Total Knee Arthroplasty Regardless of Joint Gap and Knee Size","authors":"M. Rueff, Josh Glenboski, Roger Lacaille, L. Angibaud","doi":"10.29007/9217","DOIUrl":"https://doi.org/10.29007/9217","url":null,"abstract":"Balancing soft tissues in the knee with the patella in place and with regularly applied force helps surgeons make decisions for positioning knee components in a manner that is friendly to soft tissues. A novel intraarticular device has been developed for achieving a balanced knee joint over the range of motion of the knee without requiring manual adjustments during surgery. Quasi-Constant force output was generated by the device at usual joint gaps for the knee sizes encountered during total knee arthroplasty.","PeriodicalId":385854,"journal":{"name":"EPiC Series in Health Sciences","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130545718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
PurposeDistal radius fractures (DRF) are common types of fractures with a high incident rate. DRF can be treated either by cast or surgery. To determine the clinical procedure and the operative management, standardized guidelines have become increasingly common. As operative indications are controversial, radiographic parameters (RPs) can provide objective support for effective decision making. Calculating the RPs manually from radiographs is time consuming and subject to observer variability and clinician experience. Our aim was to develop an automatic method for accurately and reliably computing 10 RPs associated with DRF in anteroposterior (AP) and lateral radiographs of a fractured hand with and without cast.MethodsThe inputs are the AP and lateral radiographs of the fractured hand with or without cast. The outputs are 10 RP values and composite images showing the landmark points and axes used in the RPs computation on the radiographs. Our method comprises three main steps: 1) segmentation of the radius and the ulna with a deep learning radiograph pixel classifier; 2) landmark points and axis extraction from the segmentations using geometric model-based methods; 3) RPs computation from the landmarks and generation of composite images. Our study tested the accuracy of step 2.The dataset consists of 20 pairs of AP and lateral radiographs. Ground truth radius and ulna segmentations were manually performed by an expert clinician co-author. Ground truth landmarks were manually located and annotated by the two expert clinician co-authors. The computed RP was considered accurate (in range) when its value was inside the inter and intra observer variability range of the manual annotation. The overall accuracy of the AP and lateral measurements was obtained by averaging the accuracy of each RP.ResultsThe accuracy of the computed AP RPs is 92.7%. The Radial Length and Radial Shift are within the observer variability range; for the Radial Angle, Ulnar Variance and Step all cases are within range except for one outlier; the Gap has two outlier cases. The accuracy of the computed lateral RPs is 100%: all four Palmer Tilt, Dorsal Shift, Gap, and Step are within the clinician observer variability.ConclusionAutomatic computation of distal radius fractures RPs from AP and lateral radiographs of hands with and without cast can be performed accurately. Precise and consistent measurement of RPs may improve the clinical decision making process.
{"title":"Automatic method for computing radiographic parameters of radial metaphyseal fractures in radiographs for surgical decision support","authors":"Avigail Suna, A. Davidson, Leo Joskowicz, Y. Weil","doi":"10.29007/phsh","DOIUrl":"https://doi.org/10.29007/phsh","url":null,"abstract":"PurposeDistal radius fractures (DRF) are common types of fractures with a high incident rate. DRF can be treated either by cast or surgery. To determine the clinical procedure and the operative management, standardized guidelines have become increasingly common. As operative indications are controversial, radiographic parameters (RPs) can provide objective support for effective decision making. Calculating the RPs manually from radiographs is time consuming and subject to observer variability and clinician experience. Our aim was to develop an automatic method for accurately and reliably computing 10 RPs associated with DRF in anteroposterior (AP) and lateral radiographs of a fractured hand with and without cast.MethodsThe inputs are the AP and lateral radiographs of the fractured hand with or without cast. The outputs are 10 RP values and composite images showing the landmark points and axes used in the RPs computation on the radiographs. Our method comprises three main steps: 1) segmentation of the radius and the ulna with a deep learning radiograph pixel classifier; 2) landmark points and axis extraction from the segmentations using geometric model-based methods; 3) RPs computation from the landmarks and generation of composite images. Our study tested the accuracy of step 2.The dataset consists of 20 pairs of AP and lateral radiographs. Ground truth radius and ulna segmentations were manually performed by an expert clinician co-author. Ground truth landmarks were manually located and annotated by the two expert clinician co-authors. The computed RP was considered accurate (in range) when its value was inside the inter and intra observer variability range of the manual annotation. The overall accuracy of the AP and lateral measurements was obtained by averaging the accuracy of each RP.ResultsThe accuracy of the computed AP RPs is 92.7%. The Radial Length and Radial Shift are within the observer variability range; for the Radial Angle, Ulnar Variance and Step all cases are within range except for one outlier; the Gap has two outlier cases. The accuracy of the computed lateral RPs is 100%: all four Palmer Tilt, Dorsal Shift, Gap, and Step are within the clinician observer variability.ConclusionAutomatic computation of distal radius fractures RPs from AP and lateral radiographs of hands with and without cast can be performed accurately. Precise and consistent measurement of RPs may improve the clinical decision making process.","PeriodicalId":385854,"journal":{"name":"EPiC Series in Health Sciences","volume":"79 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133878819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Prashant U. Pandey, B. Hohlmann, Peter Brößner, I. Hacihaliloglu, Keiran Barr, T. Ungi, O. Zettinig, R. Prevost, G. Dardenne, Zian Fanti, W. Wein, E. Stindel, F. A. Cosío, P. Guy, G. Fichtinger, K. Radermacher, A. Hodgson
Ultrasound (US) bone segmentation is an important component of US-guided or- thopaedic procedures. While there are many published segmentation techniques, there is no direct way to compare their performance. We present a solution to this, by curating a multi-institutional set of US images and corresponding segmentations, and systematically evaluating six previously-published bone segmentation algorithms using consistent metric definitions. We find that learning-based segmentation methods outperform traditional al- gorithms that rely on hand-crafted image features, as measured by their Dice scores, RMS distance errors and segmentation success rates. However, there is no single best performing algorithm across the datasets, emphasizing the need for carefully evaluating techniques on large, heterogenous datasets. The datasets and evaluation framework described can be used to accelerate development of new segmentation algorithms.
{"title":"Standardized Evaluation of Current Ultrasound Bone Segmentation Algorithms on Multiple Datasets","authors":"Prashant U. Pandey, B. Hohlmann, Peter Brößner, I. Hacihaliloglu, Keiran Barr, T. Ungi, O. Zettinig, R. Prevost, G. Dardenne, Zian Fanti, W. Wein, E. Stindel, F. A. Cosío, P. Guy, G. Fichtinger, K. Radermacher, A. Hodgson","doi":"10.29007/q51n","DOIUrl":"https://doi.org/10.29007/q51n","url":null,"abstract":"Ultrasound (US) bone segmentation is an important component of US-guided or- thopaedic procedures. While there are many published segmentation techniques, there is no direct way to compare their performance. We present a solution to this, by curating a multi-institutional set of US images and corresponding segmentations, and systematically evaluating six previously-published bone segmentation algorithms using consistent metric definitions. We find that learning-based segmentation methods outperform traditional al- gorithms that rely on hand-crafted image features, as measured by their Dice scores, RMS distance errors and segmentation success rates. However, there is no single best performing algorithm across the datasets, emphasizing the need for carefully evaluating techniques on large, heterogenous datasets. The datasets and evaluation framework described can be used to accelerate development of new segmentation algorithms.","PeriodicalId":385854,"journal":{"name":"EPiC Series in Health Sciences","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121636658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Clément Daviller, S. Polakovic, A. Greene, F. Bertrand
Reference axis based on Friedman’s approach is widely recognized as an anatomic landmark from which to measure and compare implant parameters within preoperative planning software for total shoulder arthroplasty. Equinoxe Planning Application (ExactechInc.) offers 3D measurements techniques for glenoid version and inclination requiring meticulous placement of trigonum and glenoid center. We propose as automatic determination of this reference axis, based on deep learning that shown a median error of less than 1°.
{"title":"Automatic Friedman’s Axis placement via the use of deep learning algorithms","authors":"Clément Daviller, S. Polakovic, A. Greene, F. Bertrand","doi":"10.29007/r8cp","DOIUrl":"https://doi.org/10.29007/r8cp","url":null,"abstract":"Reference axis based on Friedman’s approach is widely recognized as an anatomic landmark from which to measure and compare implant parameters within preoperative planning software for total shoulder arthroplasty. Equinoxe Planning Application (ExactechInc.) offers 3D measurements techniques for glenoid version and inclination requiring meticulous placement of trigonum and glenoid center. We propose as automatic determination of this reference axis, based on deep learning that shown a median error of less than 1°.","PeriodicalId":385854,"journal":{"name":"EPiC Series in Health Sciences","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122519638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ricardo Antunes, P. Jacob, A. Meyer, R. Marchand, M. Verstraete
Remote patient monitoring, using wearable devices and connected patient engagement platforms has the potential to improve timely clinical decisions. Data collected from multiple patients, including using the remote engagement platforms themselves, can be used to produce evidence-based reference to support clinical decisions. While some normative references for functional measure currently exist for total knee arthroplasty (TKA), these are still lacking for VAS pain scores. Therefore, VAS pain scores on a 10-point Likert scale were analyzed for 66 patients, each reporting at least five scores in the 180 days following surgery. These were used to produce a normative recovery model for total knee arthroplasty patients. A nonlinear mixed effects model was fitted, whereby the response variable is assumed to be distributed following a beta-binomial distribution. The population mean trend showed a with wide dispersion in the first few days following surgery, showing scores ranging throughout the 10-point scale. After the first week, the expected pain score steadily decreases, resulting in a score no higher than one in 50% of the population beyond 90 days after surgery. The fitted model allows referencing individual patient's pain scores at different stages of recovery, against the model’s predicted distribution. This approach can support early detection of patients that significantly deviate from the reference model and be a useful integration into clinical decision support software tools.
{"title":"Feeling Better After TKA: Reference chart for remotely collected pain scores","authors":"Ricardo Antunes, P. Jacob, A. Meyer, R. Marchand, M. Verstraete","doi":"10.29007/mlnb","DOIUrl":"https://doi.org/10.29007/mlnb","url":null,"abstract":"Remote patient monitoring, using wearable devices and connected patient engagement platforms has the potential to improve timely clinical decisions. Data collected from multiple patients, including using the remote engagement platforms themselves, can be used to produce evidence-based reference to support clinical decisions. While some normative references for functional measure currently exist for total knee arthroplasty (TKA), these are still lacking for VAS pain scores. Therefore, VAS pain scores on a 10-point Likert scale were analyzed for 66 patients, each reporting at least five scores in the 180 days following surgery. These were used to produce a normative recovery model for total knee arthroplasty patients. A nonlinear mixed effects model was fitted, whereby the response variable is assumed to be distributed following a beta-binomial distribution. The population mean trend showed a with wide dispersion in the first few days following surgery, showing scores ranging throughout the 10-point scale. After the first week, the expected pain score steadily decreases, resulting in a score no higher than one in 50% of the population beyond 90 days after surgery. The fitted model allows referencing individual patient's pain scores at different stages of recovery, against the model’s predicted distribution. This approach can support early detection of patients that significantly deviate from the reference model and be a useful integration into clinical decision support software tools.","PeriodicalId":385854,"journal":{"name":"EPiC Series in Health Sciences","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126959934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jean-Rassaire Fouefack, G. Dardenne, Bhushan S Borotikar, Tinashe Ernest Mutsvangwa, V. Burdin
In clinical routine, the capture of three-dimensional (3D) bone geometry is crucial for surgical planning, implant placement and postoperative evaluation. Nevertheless, accurate 3D reconstruction of the knee joint for the estimation of patient-specific features remains a challenge, although it has been widely studied. In this context, statistical shape models (SSM) have been used to reconstruct a global shape from partial observations, based on their ability to capture the anatomical variation from different patients. However, these studies incorporate single object SSMs which limit their application for analyzing local bone morphology and thus they lack the capacity to analyze the human anatomy at the joint level. In this paper, we present a multi-object based framework for the 3D reconstruction of the knee joint using a dynamic multi-object Gaussian process model (DMO-GPM) and an adapted Markov Chain Monte Carlo (MCMC) based model fitting algorithm.The knees were reconstructed with an average mean square error of 1.81±0.37 mm and maximum error of 3.31 mm corresponding to the surface-to-surface distance between the predicted and original knees. The results show that the knee is accurately reconstructed, especially around the joint contact surfaces. This is crucial because most of the patient- specific features required for the implant design, use landmarks in this area. The results suggest that the approach is robust and accurate to design personalized knee implants.
{"title":"3D reconstruction of joints from partial data using multi-object-based model: Towards a patient-specific knee implant design","authors":"Jean-Rassaire Fouefack, G. Dardenne, Bhushan S Borotikar, Tinashe Ernest Mutsvangwa, V. Burdin","doi":"10.29007/dcj8","DOIUrl":"https://doi.org/10.29007/dcj8","url":null,"abstract":"In clinical routine, the capture of three-dimensional (3D) bone geometry is crucial for surgical planning, implant placement and postoperative evaluation. Nevertheless, accurate 3D reconstruction of the knee joint for the estimation of patient-specific features remains a challenge, although it has been widely studied. In this context, statistical shape models (SSM) have been used to reconstruct a global shape from partial observations, based on their ability to capture the anatomical variation from different patients. However, these studies incorporate single object SSMs which limit their application for analyzing local bone morphology and thus they lack the capacity to analyze the human anatomy at the joint level. In this paper, we present a multi-object based framework for the 3D reconstruction of the knee joint using a dynamic multi-object Gaussian process model (DMO-GPM) and an adapted Markov Chain Monte Carlo (MCMC) based model fitting algorithm.The knees were reconstructed with an average mean square error of 1.81±0.37 mm and maximum error of 3.31 mm corresponding to the surface-to-surface distance between the predicted and original knees. The results show that the knee is accurately reconstructed, especially around the joint contact surfaces. This is crucial because most of the patient- specific features required for the implant design, use landmarks in this area. The results suggest that the approach is robust and accurate to design personalized knee implants.","PeriodicalId":385854,"journal":{"name":"EPiC Series in Health Sciences","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126688471","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yves Vanderschelden, A. Grassi, S. Bignozzi, Irene Asmonti, S. Zaffagnini
A procedure with subvastus lateral approach has been utilized routinely on 60 patients, navigation was used due to the reduced exposure of this technique. Purpose of this study was to evaluate pain, function, and implant kinematics at early follow up of this surgical technique.Tibial and femoral implant planning was based on ligament balance, gaps, and intraoperative kinematics. This approach, on pain and function, was verified at early follow- up. KSS and pain score were obtained at pre-op, 1, 3, 12 months. Data were analyzed with ANOVA for KSS and Chi-square for Pain.No intraoperative complications were registered, no patellar tendon lesion or avulsion was noted. Preoperative average leg alignment was 4±6° varus (range 16; -14), corrected to 0° (range 2; -1). Kinematic analysis showed rollback on lateral compartment, while on medial compartment rollback was lower or negligible until 70° of flexion. Less than 5% had a “Fair” or “Poor” KSS score after 3 months. Preop pain was: 41% severe; 50% moderate; 8% mild and 0% none. At 1 month pain was: 2% severe; 18% moderate; 55% mild and 25% none. After 3 months 50% of patients had mild and 50% had no pain. This data was maintained after 1 year, with 31% of patients with mild and 69% of patients no pain (p<0.05).This approach produced promising early outcomes in terms of pain, ROM and knee function, with less than 5% of patients presenting sub-optimal clinical results at 3- months. On symmetrical implant, medial pivot behavior was observed. Medial ligamental envelope preservation and navigated ligament balancing allow to optimize the medial stability and minimize the post-operative pain.
{"title":"Kinematics and Early Clinical Outcomes of Navigated Total Knee Arthroplasty through a Lateral Subvastus Approach","authors":"Yves Vanderschelden, A. Grassi, S. Bignozzi, Irene Asmonti, S. Zaffagnini","doi":"10.29007/qpnp","DOIUrl":"https://doi.org/10.29007/qpnp","url":null,"abstract":"A procedure with subvastus lateral approach has been utilized routinely on 60 patients, navigation was used due to the reduced exposure of this technique. Purpose of this study was to evaluate pain, function, and implant kinematics at early follow up of this surgical technique.Tibial and femoral implant planning was based on ligament balance, gaps, and intraoperative kinematics. This approach, on pain and function, was verified at early follow- up. KSS and pain score were obtained at pre-op, 1, 3, 12 months. Data were analyzed with ANOVA for KSS and Chi-square for Pain.No intraoperative complications were registered, no patellar tendon lesion or avulsion was noted. Preoperative average leg alignment was 4±6° varus (range 16; -14), corrected to 0° (range 2; -1). Kinematic analysis showed rollback on lateral compartment, while on medial compartment rollback was lower or negligible until 70° of flexion. Less than 5% had a “Fair” or “Poor” KSS score after 3 months. Preop pain was: 41% severe; 50% moderate; 8% mild and 0% none. At 1 month pain was: 2% severe; 18% moderate; 55% mild and 25% none. After 3 months 50% of patients had mild and 50% had no pain. This data was maintained after 1 year, with 31% of patients with mild and 69% of patients no pain (p<0.05).This approach produced promising early outcomes in terms of pain, ROM and knee function, with less than 5% of patients presenting sub-optimal clinical results at 3- months. On symmetrical implant, medial pivot behavior was observed. Medial ligamental envelope preservation and navigated ligament balancing allow to optimize the medial stability and minimize the post-operative pain.","PeriodicalId":385854,"journal":{"name":"EPiC Series in Health Sciences","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130154715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
H. Hess, Philipp Gussarow, J. T. Rojas, Stefan Weber, Annabel Hayoz, M. Zumstein, Kate Gerber
Rotator cuff tears (RCT) are one of the most common sources of shoulder pain. Many factors can be considered to choose the right surgical treatment procedure. Of the most important factors are the tear retraction and tear width, assessed manually on preoperative MRI.A novel approach to automatically quantify a rotator cuff tear, based on the segmentation of the tear from MRI images, was developed and validated. For segmentation, a neural network was trained and methods for the automatic calculation of the tear width and retraction from the segmented tear volume were developed.The accuracy of the automatic segmentation and the automated tear analysis were evaluated relative to manual consensus segmentations by two clinical experts. Variance in the manual segmentations was assessed in an interrater variability study of two clinical experts.The accuracy of the tear retraction calculation based on the developed automatic tear segmentation was 5.3 mm ± 5.0 mm in comparison to the interrater variability of tear retraction calculation based on manual segmentations of 3.6 mm ± 2.9 mm.These results show that an automatic quantification of a rotator cuff tear is possible. The large interrater variability of manual segmentation-based measurements highlights the difficulty of the tear segmentations task in general.
{"title":"Fully Automatic Analysis of Posterosuperior Full-Thickness Rotator Cuff Tears from MRI","authors":"H. Hess, Philipp Gussarow, J. T. Rojas, Stefan Weber, Annabel Hayoz, M. Zumstein, Kate Gerber","doi":"10.29007/fnjd","DOIUrl":"https://doi.org/10.29007/fnjd","url":null,"abstract":"Rotator cuff tears (RCT) are one of the most common sources of shoulder pain. Many factors can be considered to choose the right surgical treatment procedure. Of the most important factors are the tear retraction and tear width, assessed manually on preoperative MRI.A novel approach to automatically quantify a rotator cuff tear, based on the segmentation of the tear from MRI images, was developed and validated. For segmentation, a neural network was trained and methods for the automatic calculation of the tear width and retraction from the segmented tear volume were developed.The accuracy of the automatic segmentation and the automated tear analysis were evaluated relative to manual consensus segmentations by two clinical experts. Variance in the manual segmentations was assessed in an interrater variability study of two clinical experts.The accuracy of the tear retraction calculation based on the developed automatic tear segmentation was 5.3 mm ± 5.0 mm in comparison to the interrater variability of tear retraction calculation based on manual segmentations of 3.6 mm ± 2.9 mm.These results show that an automatic quantification of a rotator cuff tear is possible. The large interrater variability of manual segmentation-based measurements highlights the difficulty of the tear segmentations task in general.","PeriodicalId":385854,"journal":{"name":"EPiC Series in Health Sciences","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116038188","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Aouam Djamel, R. Querrec, Thierry Duval, N. Zenati, C. Hamitouche
Minimally invasive intervention requires accuracy and practice as it can be vital in complex and narrow places. In this paper we propose a solution based on augmented reality (AR) for the ablation of bone tumors. Our proposal deals with the preoperative and intraoperative phases of the procedure. The first part consists of the segmentation and 3D reconstruction of the structures of interest. The second part consists of the visualization in AR. This solution is intended to facilitate the tasks of surgeons and radiologists when planning RF needle insertion and trajectory in order to avoid excessive exposure to X-rays, which is a phase that requires more precision and knowledge of the morphology of the mass tumor. The second part offers AR assistance based on the planning of the preoperative phase. The solution we proposed is based on the use of HoloLens 2 headsets to provide better AR visualization and assistance.
{"title":"Mixed reality for minimally invasive Bone Tumor ablation","authors":"Aouam Djamel, R. Querrec, Thierry Duval, N. Zenati, C. Hamitouche","doi":"10.29007/jzrh","DOIUrl":"https://doi.org/10.29007/jzrh","url":null,"abstract":"Minimally invasive intervention requires accuracy and practice as it can be vital in complex and narrow places. In this paper we propose a solution based on augmented reality (AR) for the ablation of bone tumors. Our proposal deals with the preoperative and intraoperative phases of the procedure. The first part consists of the segmentation and 3D reconstruction of the structures of interest. The second part consists of the visualization in AR. This solution is intended to facilitate the tasks of surgeons and radiologists when planning RF needle insertion and trajectory in order to avoid excessive exposure to X-rays, which is a phase that requires more precision and knowledge of the morphology of the mass tumor. The second part offers AR assistance based on the planning of the preoperative phase. The solution we proposed is based on the use of HoloLens 2 headsets to provide better AR visualization and assistance.","PeriodicalId":385854,"journal":{"name":"EPiC Series in Health Sciences","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122915112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Souipas, Stephen Laws, F. Rodriguez y Baena, B. Davies
This paper describes Signature Robot, a cooperative haptic robot for knee surgery. Designed to address the lessons learned from the pioneering Acrobot Company ltd, this novel platform allows low and even impedance motion across 3 degrees of freedom, whilst the implementation of active constraints ensures patient safety throughout surgery. The robot was demonstrated to have an average positional accuracy of 0.82mm.
{"title":"Towards Miniaturised Collaborative Haptic Robots For Computer Aided Knee Surgery: Signature Robot","authors":"S. Souipas, Stephen Laws, F. Rodriguez y Baena, B. Davies","doi":"10.29007/h469","DOIUrl":"https://doi.org/10.29007/h469","url":null,"abstract":"This paper describes Signature Robot, a cooperative haptic robot for knee surgery. Designed to address the lessons learned from the pioneering Acrobot Company ltd, this novel platform allows low and even impedance motion across 3 degrees of freedom, whilst the implementation of active constraints ensures patient safety throughout surgery. The robot was demonstrated to have an average positional accuracy of 0.82mm.","PeriodicalId":385854,"journal":{"name":"EPiC Series in Health Sciences","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129164914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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