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检索条件"任意字段=Conference on SPIE Image-Guided Procedures, Robotic Interventions, and Modeling"

共 167 条记录，以下是21-30 订阅

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DeepInfer: Open-Source Deep Learning Deployment Toolkit for image-guided Therapy

DeepInfer: Open-Source Deep Learning Deployment Toolkit for ...

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conference on Medical Imaging - image-guided procedures, robotic interventions, and modeling

作者： Mehrtash, Alireza Pesteie, Mehran Hetherington, Jorden Behringer, Peter A. Kapur, Tina Wells, William M., III Rohling, Robert Fedorov, Andriy Abolmaesumi, Purang Univ British Columbia Dept Elect & Comp Engn Vancouver BC Canada Brigham & Womens Hosp Dept Radiol 75 Francis St Boston MA 02115 USA Univ British Columbia Dept Mech Engn Vancouver BC Canada

ISBN: (数字)9781510607163

ISBN: (纸本)9781510607156;9781510607163

Deep learning models have outperformed some of the previous state-of-the-art approaches in medical image analysis. Instead of using hand-engineered features, deep models attempt to automatically extract hierarchical representations at multiple levels of abstraction from the data. Therefore, deep models are usually considered to be more flexible and robust solutions for image analysis problems compared to conventional computer vision models. They have demonstrated significant improvements in computer-aided diagnosis and automatic medical image analysis applied to such tasks as image segmentation, classification and registration. However, deploying deep learning models often has a steep learning curve and requires detailed knowledge of various software packages. Thus, many deep models have not been integrated into the clinical research workflows causing a gap between the state-of-the-art machine learning in medical applications and evaluation in clinical research procedures. In this paper, we propose "DeepInfer" an open-source toolkit for developing and deploying deep learning models within the 3D Slicer medical image analysis platform. Utilizing a repository of task-specific models, DeepInfer allows clinical researchers and biomedical engineers to deploy a trained model selected from the public registry, and apply it to new data without the need for software development or configuration. As two practical use cases, we demonstrate the application of DeepInfer in prostate segmentation for targeted MRI-guided biopsy and identification of the target plane in 3D ultrasound for spinal injections.

关键词： 3D modeling Data modeling Medical imaging image segmentation Prostate Visual process modeling Visualization Solid modeling Ultrasonography 3D image processing Biomedical engineering Biomedical optics Machine learning Neural networks Software development

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Technical Note: On Cardiac Ablation Lesion Visualization for image-guided Therapy Monitoring

Technical Note: On Cardiac Ablation Lesion Visualization for...

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conference on Medical Imaging - image-guided procedures, robotic interventions, and modeling

作者： Linte, Cristian A. Camp, Jon J. Rettmann, Maryam E. Haemmerich, Dieter Aktas, Mehmet K. Huang, David T. Packer, Douglas L. Holmes, David R., III Rochester Inst Technol Biomed Engn Rochester NY 14623 USA Rochester Inst Technol Chester F Carlson Ctr Imaging Sci Rochester NY 14623 USA Mayo Clin Biomed Imaging Resource Rochester MN 55902 USA Mayo Clin Div Cardiol Rochester MN USA Med Univ South Carolina Dept Pediat Charleston SC USA Univ Rochester Med Ctr Div Cardiol Rochester NY 14642 USA

ISBN: (数字)9781510616424

ISBN: (纸本)9781510616424

The delivery of insufficient thermal dose is a significant contributor to incomplete tissue ablation and leads to arrhythmia recurrence and a large number of patients requiring repeat procedures. In concert with ongoing research efforts aimed at better characterizing the RF energy delivery, here we propose a method that entails modeling and visualization of the lesions in real time. The described image-based ablation model relies on classical heat transfer principles to estimate tissue temperature in response to the ablation parameters, tissue properties, and duration. The ablation lesion quality, geometry, and overall progression is quantified on a voxel-by-voxel basis according to each voxel's cumulative temperature and time exposure. The model was evaluated both numerically under different parameter conditions, as well as experimentally, using ex vivo bovine tissue samples. This study suggests that the proposed technique provides reasonably accurate and sufficiently fast visualizations of the delivered ablation lesions.

关键词： image-guided cardiac catheter ablation radiofrequency ablation ablation lesion modeling and monitoring model evaluation and validation lesion visualization

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The image-to-Physical Liver Registration Sparse Data Challenge: Characterizing Inverse Biomechanical Model Resolution

The Image-to-Physical Liver Registration Sparse Data Challen...

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Medical Imaging conference - image-guided procedures, robotic interventions, and modeling

作者： Heiselman, Jon S. Miga, Michael, I Vanderbilt Univ Dept Biomed Engn Nashville TN 37235 USA Vanderbilt Univ Vanderbilt Inst Surg & Engn 221 Kirkland Hall Nashville TN 37235 USA

ISBN: (纸本)9781510633988

image-guided liver surgery relies on intraoperatively acquired data to create an accurate alignment between image space and the physical patient anatomy. Often, sparse data of the anterior liver surface can be collected for these registrations. However, achieving accurate registration to sparse surface data when soft tissue deformation is present remains a challenging open problem. While many approaches have been developed, a common standard for comparing algorithm performance has yet to be adopted. The image-to-physical liver registration sparse data challenge offers a publicly available dataset of realistic sparse data patterns collected on a deforming liver phantom for the purpose of evaluating and comparing potential registration approaches. Additionally, the challenge is designed to allow testing and characterization of these methods as a general utility for the registration community. Using this challenge environment, an inverse biomechanical method for deformable registration to sparse data was investigated with respect to how whole-organ target registration error (TRE) is impacted by a model parameter that controls the spatial reconstructive resolution of mechanical loads applied to the organ. For this analysis, this resolution parameter was varied across a wide range of values and TRE was calculated from the challenge dataset. An optimal parameter value for model resolution was found and average TRE across the 112 sparse data challenge cases was reduced to 3.08 +/- 0.85 mm, an approximate 32% improvement over previously reported results. The value of the data offered by the sparse data challenge is evident. This work was performed entirely using information automatically generated by the challenge submission and processing site.

关键词： Liver image registration Data modeling Natural surfaces Surgery Data acquisition Reconstruction algorithms Algorithm development

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A dynamic mitral valve simulator for surgical training and patient specific preoperative planning

A dynamic mitral valve simulator for surgical training and p...

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conference on Medical Imaging - image-guided procedures, robotic interventions, and modeling

作者： Boone, Nora Moore, John Ginty, Olivia Bainbridge, Daniel Eskandari, Mehdi Peters, Terry Western Univ Dept Med Sci London ON Canada Western Univ Robarts Res Inst London ON Canada Western Univ Dept Anesthesiol London ON Canada Western Univ Sch Biomed Engn London ON Canada Kings Coll Hosp London Denmark Hill London England

ISBN: (纸本)9781510625501

Mitral valve disease affects 2% of the Canadian population and 10% of those over the age of 75. Mitral valve regurgitation is a common valve disease often requiring surgical intervention for repair or replacement. Repair is often preferred over replacement, as it is associated with improved outcomes. Current mitral valve repair training is typically limited primarily to intraoperative experience. Additionally, the outcome of complex repair procedures is often unknown preoperatively, and is particularly true of new, off-pump repair techniques. Further challenges include identifying the most effective repair technique based on patient pathology, as multiple approaches exist. We present a hemodynamically accurate mitral valve phantom for testing previously validated patient specific pathological mitral valves. The device can be used for surgical resident training as well as complex procedure planning. The simulator is validated using pressure measurements across the mitral valve demonstrating realistic hemodynamics across a range of heart rates, and by evaluating valve function using 2D and 3D transesophageal echocardiography (TEE).

关键词： Heart 3D modeling Data modeling Silicon Surgery Pathology modeling 3D image processing Hemodynamics Cardiac imaging modeling and simulation

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3D Catheter Reconstruction using Non-Rigid Structure-from-Motion and robotics modeling

3D Catheter Reconstruction using Non-Rigid Structure-from-Mo...

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conference on Medical Imaging - image-guided procedures, robotic interventions and modeling

作者： Papalazarou, Chrysi Rongen, Peter M. J. de With, Peter H. N. Eindhoven Univ Technol POB 513 NL-5600 MB Eindhoven Netherlands Philips Healthcare Best Netherlands

ISBN: (纸本)9780819489654

Surgical guidance during minimally invasive intervention could be greatly enhanced if the 3D location and orientation of instruments, especially catheters, is available. In this paper, we present a new method for the 3D reconstruction of deforming curvilinear objects such as catheters, using the framework of Non-Rigid Structure-from-Motion (NRSfM). We combine NRSfM with a kinematics model from the field of robotics, which provides a low-dimensional parametrization of the object deformation. This is used in the context of an X-ray imaging system where multiple views are acquired with a small view separation. We show that using such a kinematics model, a non-linear optimization scheme succeeds in retrieving the deformable 3D pose from the 2D projections. Experiments on synthetic and real X-ray data show promising results of the proposed method as compared to state-of-the-art NRSfM.

关键词： Multi-view X-ray catheter reconstruction NRSfM kinematic modeling 3D shape recovery

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Anatomical Parameterization for Volumetric Meshing of the Liver

Anatomical Parameterization for Volumetric Meshing of the Li...

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conference on Medical Imaging - image-guided procedures, robotic interventions, and modeling

作者： Vera, Sergio Gonzalez, Miguel A. Gil, Debora Alma IT Syst Barcelona Spain Univ Autonoma Barcelona Ctr Comp Vis Comp Sci Dept Barcelona Spain ICREA Catalan Inst Res & Adv Studies Barcelona Spain Univ Pompeu Fabra Barcelona Spain

ISBN: (纸本)9780819498298

A coordinate system describing the interior of organs is a powerful tool for a systematic localization of injured tissue. If the same coordinate values are assigned to specific anatomical landmarks, the coordinate system allows integration of data across different medical image modalities. Harmonic mappings have been used to produce parametric coordinate systems over the surface of anatomical shapes, given their flexibility to set values at specific locations through boundary conditions. However, most of the existing implementations in medical imaging restrict to either anatomical surfaces, or the depth coordinate with boundary conditions is given at sites of limited geometric diversity. In this paper we present a method for anatomical volumetric parameterization that extends current harmonic parameterizations to the interior anatomy using information provided by the volume medial surface. We have applied the methodology to define a common reference system for the liver shape and functional anatomy. This reference system sets a solid base for creating anatomical models of the patient's liver, and allows comparing livers from several patients in a common framework of reference.

关键词： C oordinate System Anatomy modeling Parameterization

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Automatic thoracic anatomy segmentation on CT images using hierarchical fuzzy models and registration

Automatic thoracic anatomy segmentation on CT images using h...

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conference on Medical Imaging - image-guided procedures, robotic interventions, and modeling

作者： Sun, Kaiqiong Udupa, Jayaram K. Odhner, Dewey Tong, Yubing Torigian, Drew A. Nanchang Hangkong Univ Comp Vis Lab Nanchang 330063 Peoples R China Univ Pennsylvania Med Image Proc Grp 423 Guardian DrBlockley Hall4th Floor Philadelphia PA 19104 USA Univ Pennsylvania Dept Radiol 423 Guardian DrBlockley Hall4th Floor Philadelphia PA 19104 USA

ISBN: (纸本)9780819498298

This paper proposes a thoracic anatomy segmentation method based on hierarchical recognition and delineation guided by a built fuzzy model. Labeled binary samples for each organ are registered and aligned into a 3D fuzzy set representing the fuzzy shape model for the organ. The gray intensity distributions of the corresponding regions of the organ in the original image are recorded in the model. The hierarchical relation and mean location relation between different organs are also captured in the model. Following the hierarchical structure and location relation, the fuzzy shape model of different organs is registered to the given target image to achieve object recognition. A fuzzy connected delineation method is then used to obtain the final segmentation result of organs with seed points provided by recognition. The hierarchical structure and location relation integrated in the model provide the initial parameters for registration and make the recognition efficient and robust. The 3D fuzzy model combined with hierarchical affine registration ensures that accurate recognition can be obtained for both non-sparse and sparse organs. The results on real images are presented and shown to be better than a recently reported fuzzy model-based anatomy recognition strategy.

关键词： Shape modeling fuzzy models object recognition fuzzy connectedness segmentation registration

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Validation of model-based deformation correction in image-guided liver surgery via tracked intraoperative ultrasound: preliminary method and results

Validation of model-based deformation correction in image-gu...

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conference on Medical Imaging - image-guided procedures, robotic interventions, and modeling

作者： Clements, Logan W. Collins, Jarrod A. Wu, Yifei Simpson, Amber L. Jarnagin, William R. Miga, Michael I. Vanderbilt Univ Dept Biomed Engn Nashville TN 37235 USA Mem Sloan Kettering Canc Ctr Dept Surg New York NY 10021 USA Vanderbilt Univ Dept Radiol & Radiol Sci Nashville TN 37235 USA Vanderbilt Univ Dept Neurol Surg Nashville TN 37235 USA

ISBN: (纸本)9781628415056

Soft tissue deformation represents a significant error source in current surgical navigation systems used for open hepatic procedures. While numerous algorithms have been proposed to rectify the tissue deformation that is encountered during open liver surgery, clinical validation of the proposed methods has been limited to surface based metrics and sub-surface validation has largely been performed via phantom experiments. Tracked intraoperative ultrasound (iUS) provides a means to digitize sub-surface anatomical landmarks during clinical procedures. The proposed method involves the validation of a deformation correction algorithm for open hepatic image-guided surgery systems via sub-surface targets digitized with tracked iUS. Intraoperative surface digitizations were acquired via a laser range scanner and an optically tracked stylus for the purposes of computing the physical-to-image space registration within the guidance system and for use in retrospective deformation correction. Upon completion of surface digitization, the organ was interrogated with a tracked iUS transducer where the iUS images and corresponding tracked locations were recorded. After the procedure, the clinician reviewed the iUS images to delineate contours of anatomical target features for use in the validation procedure. Mean closest point distances between the feature contours delineated in the iUS images and corresponding 3-D anatomical model generated from the preoperative tomograms were computed to quantify the extent to which the deformation correction algorithm improved registration accuracy. The preliminary results for two patients indicate that the deformation correction method resulted in a reduction in target error of approximately 50%.

关键词： clinical validation biomechanical modeling image-guided surgery open liver surgery soft tissue deformation tracked ultrasound

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Rapid Virtual Stenting for Intracranial Aneurysms

Rapid Virtual Stenting for Intracranial Aneurysms

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conference on spie image-guided procedures, robotic interventions, and modeling

作者： Zhao, Liang Chen, Danyang Chen, Zihe Wang, Xiangyu Paliwal, Nikhil Xiang, Jianping Meng, Hui Corso, Jason J. Xu, Jinhui Univ Buffalo Buffalo NY 14260 USA Univ Michigan Ann Arbor MI 48109 USA

ISBN: (纸本)9781510600218

The rupture of Intracranial Aneurysms is the most severe form of stroke with high rates of mortality and disability. One of its primary treatments is to use stent or Flow Diverter to divert the blood flow away from the IA in a minimal invasive manner. To optimize such treatments, it is desirable to provide an automatic tool for virtual stenting before its actual implantation. In this paper, we propose a novel method, called ball-sweeping, for rapid virtual stenting. Our method sweeps a maximum inscribed sphere through the aneurysmal region of the vessel and directly generates a stent surface touching the vessel wall without needing to iteratively grow a deformable stent surface. Our resulting stent mesh has guaranteed smoothness and variable pore density to achieve an enhanced occlusion performance. Comparing to existing methods, our technique is computationally much more efficient.

关键词： Surface plasmons Finite element methods Computer programming Computer simulations Arteries Medical imaging robotics Vascular diseases Visual process modeling Visualization

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image-guided preoperative prediction of pyramidal tract side effect in deep brain stimulation

Image-guided preoperative prediction of pyramidal tract side...

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conference on spie image-guided procedures, robotic interventions, and modeling

作者： Baumgarten, C. Zhao, Y. Sauleau, P. Malrain, C. Jannin, P. Haegelen, C. INSERM UMR 1099 Rennes France Univ Rennes 1 LTSI Rennes France Univ Hosp Rennes Dept Neurol Rennes France Univ Hosp Rennes Dept Neurosurg Rennes France

ISBN: (纸本)9781510600218

Deep brain stimulation of the medial globus pallidus is a surgical procedure for treating patients suffering from Parkinson's disease. Its therapeutic effect may be limited by the presence of pyramidal tract side effect (PTSE). PTSE is a contraction time-locked to the stimulation when the current spreading reaches the motor fibers of the pyramidal tract within the internal capsule. The lack of side-effect predictive model leads the neurologist to secure an optimal electrode placement by iterating clinical testing on an awake patient during the surgical procedure. The objective of the study was to propose a preoperative predictive model of PTSE. A machine learning based method called PyMAN (for Pyramidal tract side effect Model based on Artificial Neural network) that accounted for the current of the stimulation, the 3D electrode coordinates and the angle of the trajectory, was designed to predict the occurrence of PTSE. Ten patients implanted in the medial globus pallidus have been tested by a clinician to create a labeled dataset of the stimulation parameters that trigger PTSE. The kappa index value between the data predicted by PyMAN and the labeled data was .78. Further evaluation studies are desirable to confirm whether PyMAN could be a reliable tool for assisting the surgeon to prevent PTSE during the preoperative planning.

关键词： Artificial neural network Computer-assisted surgery Computational modeling Deep brain stimulation Parkinson's disease Patient-specific model Pyramidal tract side effect

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