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

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AUTOMATIC ANATOMY RECOGNITION VIA FUZZY OBJECT MODELS

AUTOMATIC ANATOMY RECOGNITION VIA FUZZY OBJECT MODELS

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

作者： Udupa, Jayaram K. Odhner, Dewey Falcao, Alexandre X. Ciesielski, Krzysztof Chris Miranda, Paulo A. V. Matsumoto, Monica Grevera, George J. Saboury, Babak Torigian, Drew A. UPENN Med Image Proc Grp Philadelphia PA 19104 USA Univ Estadual Campinas Inst Comp Campinas SP Brazil West Virginia Univ Dept Math Morgantown WV 26506 USA St Josephs Univ Dept Math & Comp Sci Philadelphia PA 19131 USA Hosp Univ Penn Dept Radiol Philadelphia PA 19131 USA

ISBN: (纸本)9780819489654

To make Quantitative Radiology a reality in routine radiological practice, computerized automatic anatomy recognition (AAR) during radiological image reading becomes essential. As part of this larger goal, last year at this conference we presented a fuzzy strategy for building body-wide group-wise anatomic models. In the present paper, we describe the further advances made in fuzzy modeling and the algorithms and results achieved for AAR by using the fuzzy models. The proposed AAR approach consists of three distinct steps: (a) Building fuzzy object models (FOMs) for each population group G. (b) By using the FOMs to recognize the individual objects in any given patient image I under group G. (c) To delineate the recognized objects in I. This paper will focus mostly on (b). FOMs are built hierarchically, the smaller sub-objects forming the offspring of larger parent objects. The hierarchical pose relationships from the parent to offspring are codified in the FOMs. Several approaches are being explored currently, grouped under two strategies, both being hierarchical: (ra1) those using search strategies;(ra2) those strategizing a one-shot approach by which the model pose is directly estimated without searching. Based on 32 patient CT data sets each from the thorax and abdomen and 25 objects modeled, our analysis indicates that objects do not all scale uniformly with patient size. Even the simplest among the (ra2) strategies of recognizing the root object and then placing all other descendants as per the learned parent-to-offspring pose relationship bring the models on an average within about 18 mm of the true locations.

关键词： Shape modeling Fuzzy Sets Object Recognition Segmentation Fuzzy Connectedness Fuzzy Models

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Integration of a biomechanical simulation for mitral valve reconstruction into a knowledge-based surgery assistance system

Integration of a biomechanical simulation for mitral valve r...

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

作者： Schoch, Nicolai Engelhardt, Sandy Zimmermann, Norbert Speidel, Stefanie De Simone, Raffaele Wolf, Ivo Heuveline, Vincent Heidelberg Univ Engn Math & Comp Lab Heidelberg Germany German Canc Res Ctr Med & Biol Informat Heidelberg Germany Univ Heidelberg Hosp Cardiac Surg Heidelberg Germany Karlsruhe Inst Technol Inst Anthropomat D-76021 Karlsruhe Germany Mannheim Univ Appl Sci Mannheim Germany

ISBN: (纸本)9781628415056

A mitral valve reconstruction (MVR) is a complex operation in which the functionality of incompetent mitral valves is re-established by applying surgical techniques. This work deals with predictive biomechanical simulations of operation scenarios for an MVR, and the simulation's integration into a knowledge-based surgery assistance system. We present a framework for the definition of the corresponding surgical workflow, which combines semantically enriched surgical expert knowledge with a biomechanical simulation. Using an ontology, 'surgical rules' which describe decision and assessment criteria for surgical decision-making are represented in a knowledge base. Through reasoning these 'rules' can then be applied on patient-specific data in order to be converted into boundary conditions for the biomechanical soft tissue simulation, which is based on the Finite Elements Method (FEM). The simulation, which is implemented in the open-source C++ FEM software HiFlow(3), is controlled via the Medical Simulation Markup Language (MSML), and makes use of High Performance Computing (HPC) methods to cope with real-time requirements in surgery. The simulation results are presented to surgeons to assess the quality of the virtual reconstruction and the consequential remedial effects on the mitral valve and its functionality. The whole setup has the potential to support the intraoperative decision-making process during MVR where the surgeon usually has to make fundamental decision under time pressure.

关键词： Biomechanical FEM Simulation High Performance Computing (HPC) Surgical Reasoning Surgery Assistance Mitral Valve modeling Mitral Valve Simulation Mitral Valve Reconstruction Medical Simulation Markup Language (MSML) Integrated Biomechanical Simulation Framework

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LV Systolic Point-Cloud Model to Quantify Accuracy of CT Derived Regional Strain

LV Systolic Point-Cloud Model to Quantify Accuracy of CT Der...

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

作者： Manohar, Ashish Colvert, Gabrielle Schluchter, Andrew Contijoch, Francisco McVeigh, Elliot R. Univ Calif San Diego Dept Mech & Aerosp Engn La Jolla CA 92093 USA Univ Calif San Diego Dept Bioengn La Jolla CA 92093 USA Univ Calif San Diego Dept Radiol La Jolla CA 92093 USA Univ Calif San Diego Dept Med Div Cardiol La Jolla CA 92093 USA

ISBN: (纸本)9781510625501

We present an analytical LV systolic model derived from human CT data to serve as the ground truth for optimization and validation of a previously published CT-derived regional strain metric called SQUEEZ. Physiologically-accurate strains were applied to each vertex of a clinically derived end-diastolic LV mesh to create analytical end-systolic poses exhibiting normal function as well as regional hypokinesia of four sizes (17.5mm, 14mm, 10.5mm, and 7mm in diameter), each with a programmed severe, medium, and subtle dysfunction. Regional strain estimates were obtained by registering the end-diastolic mesh to each end-systolic mesh condition using a non-rigid registration algorithm. Ground-truth models of normal function and of severe hypokinesia were used to identify the optimal parameters in the registration algorithm, and to measure the accuracy of detecting regional dysfunction of varying sizes and severities. We found that for normal LV systolic contraction, SQUEEZ values in all 16 AHA segments of the LV were accurately measured (within +/- 0.05). For cases with regional dysfunction, the errors in SQUEEZ in the region around the dysfunction increased with decreasing size of regional dysfunction. The mean SQUEEZ values of the 17.5mm and 14mm diameter dysfunctional regions, which we hypothesize are the most clinically relevant, were accurate to within +/- 0.05.

关键词： X-ray computed tomography Cardiac imaging Regional cardiac function Ischemic heart disease Cardiac modeling Cardiac registration

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Conformal Radiofrequency Ablation to Validate Ultrasound Thermometry

Conformal Radiofrequency Ablation to Validate Ultrasound The...

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

作者： Audigier, Chloe Kim, Younsu Ziegle, Jens Friebe, Michael Boctor, Emad M. Johns Hopkins Univ Dept Comp Sci Baltimore MD 21218 USA Johns Hopkins Univ Dept Radiol Baltimore MD 21218 USA Otto Von Guericke Univ Dept Med Engn Magdeburg Germany

ISBN: (纸本)9781510625501

Thermal ablation is a clinical procedure that aims at destroying pathological tissue minimally invasively through temperature changes. Temperature monitoring during the treatment is instrumental to achieve a precise and successful ablation procedure: ensuring a complete target ablation while preserving as much healthy tissue as possible. Ultrasound (US) is a promising low cost and portable modality, that could provide real-time temperature monitoring. However, the validation of such a technique is challenging. It is usually done with thermometers. They provide temperature measurements with good temporal resolution but only at a few local points. Magnetic Resonance Imaging (MRI) is the gold standard in term of temperature monitoring nowadays. It could also be used for validation of other thermometry techniques with a more accurate spatial resolution, but it requires MR-compatible devices. In this paper, we propose to leverage the use of a novel bipolar radiofrequency (RF) ablation device that provides 10 different ablation shapes to validate an ultrasound-based temperature monitoring method. The monitoring method relies on an external ultrasound element integrated with the bipolar RF ablation probe. This element send through the ablated tissues ultrasound waves that carry time-of-flight information. The ultrasound waves are collected by a clinical diagnostic ultrasound probe and can be related to the changes in temperature due to the ablation since ultrasound propagation velocity in biological tissue changes as temperature increases. We use this ultrasound-based method to monitor temperature during RF ablation. First on simulation data and then on two ex-vivo porcine liver experiments. Those dataset are used to show that we can validate the proposed temperature reconstruction method using the novel conformal radiofrequency ablation device by generating different ablation shapes.

关键词： Radiofrequency Ablation (RFA) Thermal Ablation Computational modeling Ultrasound thermal monitoring Validation

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Feasibility of 3D Motion-Compensated Needle Guidance for TIPS procedures

Feasibility of 3D Motion-Compensated Needle Guidance for TIP...

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

作者： Wagner, Martin G. Periyasamy, Sarvesh Speidel, Michael A. Laeseke, Paul F. Univ Wisconsin Dept Med Phys 1111 Highland Ave Madison WI 53705 USA Univ Wisconsin Dept Radiol 600 Highland Ave Madison WI 53792 USA Univ Wisconsin Dept Med 1685 Highland Ave Madison WI 53705 USA

ISBN: (纸本)9781510633988

X-ray fluoroscopy is commonly used to guide needles during transjugular intrahepatic portosystemic shunt (TIPS) procedures. Respiratory motion and the 2D nature of the x-ray projections, however, make it difficult to accurately guide the needle from a hepatic vein, through the liver parenchyma, and into the portal vein, which is not visible on x-ray images in the absence of continuous contrast-enhancement. Due to these challenges, multiple needle passes are often required, which increase the risk for perforation of the liver capsule and hemorrhage. To overcome these challenges, we propose a motion-compensated 3D needle guidance system, which generates a respiratory motion model of the portal venous system using a 3D DSA and a contrast enhanced 2D x-ray sequence acquired under free breathing conditions. The respiratory motion is tracked during the needle pass based on brightness variations above and below the diaphragm in the 2D images, which allows for creation of a motion-compensated surface model of the target vasculature. Additionally, a 3D needle reconstruction algorithm from two 2D x-ray images is presented, which allows for motion-compensated 3D device imaging. A preliminary pig study was performed to evaluate the feasibility of the proposed techniques. The biplane needle reconstruction was compared to conventional cone beam CT acquisitions, where a root mean squared distance of 0.98 mm and a tip localization error of 1.22 mm were measured. The maximum error of the estimated vascular motion per frame in the two pig studies was 0.63 mm and 1.63 mm respectively. If successfully translated to clinical TIPS procedures, the proposed needle guidance could result in fewer unnecessary needle passes and therefore shorter procedure times and lower risk to the patient.

关键词： Veins 3D modeling X-rays image segmentation 3D image processing Motion models X-ray imaging 3D acquisition Liver Reconstruction algorithms

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Fuzzy model-based body-wide anatomy recognition in medical images

Fuzzy model-based body-wide anatomy recognition in medical i...

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

作者： Udupa, Jayaram K. Odhner, Dewey Tong, Yubing Matsumoto, Monica M. S. Ciesielski, Krzysztof C. Vaideeswaran, Pavithra Ciesielski, Victoria Saboury, Babak Zhao, Liming Mohammadianrasanani, Syedmehrdad Torigian, Drew A. Univ Penn Med Image Proc Grp 423 Guardian DrBlockley Hall4th Floor Philadelphia PA 19104 USA Univ Penn Dept Radiol Philadelphia PA 19104 USA West Virginia Univ Dept Math Morgantown WV USA

ISBN: (纸本)9780819494450

To make Quantitative Radiology a reality in routine radiological practice, computerized automatic anatomy recognition (AAR) becomes essential. Previously, we presented a fuzzy object modeling strategy for AAR. This paper presents several advances in this project including streamlined definition of open-ended anatomic objects, extension to multiple imaging modalities, and demonstration of the same AAR approach on multiple body regions. The AAR approach consists of the following steps: (a) Collecting image data for each population group G and body region B. (b) Delineating in these images the objects in B to be modeled. (c) Building Fuzzy Object Models (FOMs) for B. (d) Recognizing individual objects in a given image of B by using the models. (e) Delineating the recognized objects. (f) Implementing the computationally intensive steps in a graphics processing unit (GPU). image data are collected for B and G from our existing patient image database. Fuzzy models for the individual objects are built and assembled into a model of B as per a chosen hierarchy of the objects in B. A global recognition strategy is used to determine the pose of the objects within a given image I following the hierarchy. The recognized pose is utilized to delineate the objects, also hierarchically. Based on three body regions tested utilizing both computed tomography (CT) and magnetic resonance (MR) imagery, recognition accuracy for non-sparse objects has been found to be generally sufficient (3 to 11 mm or 2-3 voxels) to yield delineation false positive (FP) and true positive (TP) values of < 5% and >= 90%, respectively. The sparse objects require further work to improve their recognition accuracy.

关键词： Shape modeling fuzzy models object recognition segmentation graph cut fuzzy connectedness

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Skull registration for prone patient position using tracked ultrasound

Skull registration for prone patient position using tracked ...

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

作者： Underwood, Grace Ungi, Tamas Baum, Zachary Lasso, Andras Kronreif, Gernot Fichtinger, Gabor Queens Univ Lab Percutaneous Surg Kingston ON Canada Austrian Ctr Med Innovat & Technol Wiener Neustadt Austria

ISBN: (数字)9781510607163

ISBN: (纸本)9781510607156;9781510607163

PURPOSE: Tracked navigation has become prevalent in neurosurgery. Problems with registration of a patient and a preoperative image arise when the patient is in a prone position. Surfaces accessible to optical tracking on the back of the head are unreliable for registration. We investigated the accuracy of surface-based registration using points accessible through tracked ultrasound. Using ultrasound allows access to bone surfaces that are not available through optical tracking. Tracked ultrasound could eliminate the need to work (i) under the table for registration and (ii) adjust the tracker between surgery and registration. In addition, tracked ultrasound could provide a non- invasive method in comparison to an alternative method of registration involving screw implantation. METHODS: A phantom study was performed to test the feasibility of tracked ultrasound for registration. An initial registration was performed to partially align the pre-operative computer tomography data and skull phantom. The initial registration was performed by an anatomical landmark registration. Surface points accessible by tracked ultrasound were collected and used to perform an Iterative Closest Point Algorithm. RESULTS: When the surface registration was compared to a ground truth landmark registration, the average TRE was found to be 1.6 +/- 0.1mm and the average distance of points off the skull surface was 0.6 +/- 0.1mm. CONCLUSION: The use of tracked ultrasound is feasible for registration of patients in prone position and eliminates the need to perform registration under the table. The translational component of error found was minimal. Therefore, the amount of TRE in registration is due to a rotational component of error.

关键词： Ultrasonography Skull image registration Optical tracking 3D modeling Bone image segmentation Computed tomography Transform theory Surgery Head Tomography

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Segmentation and Removal of Surgical Instruments for Background Scene Visualization from Endoscopic/Laparoscopic Video

Segmentation and Removal of Surgical Instruments for Backgro...

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

作者： Hasan, S. M. Kamrul Simon, Richard A. Linte, Cristian A. RIT Biomed Modeling Visualizat & Image guided Nav BiM Rochester NY 14623 USA Rochester Inst Technol Ctr Imaging Sci Rochester NY 14623 USA Rochester Inst Technol Biomed Engn Rochester NY 14623 USA

ISBN: (纸本)9781510640269

Surgical tool segmentation is becoming imperative to provide detailed information during intra-operative execution. These tools can obscure surgeons' dexterity control due to narrow working space and visual field-of-view, which increases the risk of complications resulting from tissue injuries (e.g. tissue scars and tears). This paper demonstrates a novel application of segmenting and removing surgical instruments from laparoscopic/endoscopic video using digital inpainting algorithms. To segment the surgical instruments, we use a modified U-Net architecture (U-NetPlus) composed of a pre-trained VGG11 or VGG16 encoder and redesigned decoder. The decoder is modified by replacing the transposed convolution operation with an up-sampling operation based on nearest-neighbor (NN) interpolation. This modification removes the artifacts generated by the transposed convolution, and, furthermore, these new interpolation weights require no learning for the upsampling operation. The tool removal algorithms use the tool segmentation mask and either the instrument-free reference frames or previous instrument-containing frames to fill-in (i.e., inpaint) the instrument segmentation mask with the background tissue underneath. We have demonstrated the performance of the proposed surgical tool segmentation/removal algorithms on a robotic instrument dataset from the MICCAI 2015 EndoVis Challenge. We also showed successful performance of the tool removal algorithm from synthetically generated surgical instruments-containing videos obtained by embedding a moving surgical tool into surgical tool-free videos. Our application successfully segments and removes the surgical tool to unveil the background tissue view otherwise obstructed by the tool, producing visually comparable results to the ground truth.

关键词： Surgical tool segmentation tool removal video inpainting non-parametric optical flow affine parametric motion Poisson blending

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Optimal strategies for modeling anatomy in a hybrid intelligence framework for auto-segmentation of organs

Optimal strategies for modeling anatomy in a hybrid intellig...

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

作者： Hao, You Udupa, Jayaram K. Tong, Yubing Liu, Tiange Wu, Caiyun Odhner, Dewey Torigian, Drew A. Univ Penn Dept Radiol Med Image Proc Grp 602 Goddard Bldg3710 Hamilton Walk Philadelphia PA 19104 USA Yanshan Univ Sch Informat Sci & Engn Qinhuangdao 066004 Hebei Peoples R China

ISBN: (纸本)9781510671614;9781510671607

Organ segmentation is a fundamental requirement in medical image analysis. Many methods have been proposed over the past 6 decades for segmentation. A unique feature of medical images is the anatomical information hidden within the image itself. To bring natural intelligence (NI) in the form of anatomical information accumulated over centuries into deep learning (DL) AI methods effectively, we have recently introduced the idea of hybrid intelligence (HI) that combines NI and AI and a system based on HI to perform medical image segmentation. This HI system has shown remarkable robustness to image artifacts, pathology, deformations, etc. in segmenting organs in the Thorax body region in a multicenter clinical study. The HI system utilizes an anatomy modeling strategy to encode NI and to identify a rough container region in the shape of each object via a non-DL-based approach so that DL training and execution are applied only to the fuzzy container region. In this paper, we introduce several advances related to modeling of the NI component so that it becomes substantially more efficient computationally, and at the same time, is well integrated with the DL portion (AI component) of the system. We demonstrate a 9-40 fold computational improvement in the auto-segmentation task for radiation therapy (RT) planning via clinical studies obtained from 4 different RT centers, while retaining state-of- the-art accuracy of the previous system in segmenting 11 objects in the Thorax body region.

关键词： organ segmentation deep learning hybrid intelligence anatomic models radiation therapy planning computed tomography

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Scaphoid Fracture Fixation: Localization of Bones through Statistical Model to Ultrasound Registration

Scaphoid Fracture Fixation: Localization of Bones through St...

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

作者： Abu Anas, Emran Mohammad Rasoulian, Abtin St John, Paul Pichora, David Mousavi, Parvin Lessoway, Victoria A. Seitel, Alexander Hacihaliloglu, Ilker Rohling, Robert Abolmaesumi, Purang Univ British Columbia Elect & Comp Engn Vancouver BC V5Z 1M9 Canada Kingston Gen Hosp Kingston ON K7L 2V7 Canada Queens Univ Kingston Sch Comp Kingston ON K7L 3N6 Canada BC Womens Hosp Vancouver BC Canada Rutgers State Univ Biomed Engn Piscataway NJ 08855 USA Univ British Columbia Mech Engn Vancouver BC V5Z 1M9 Canada

ISBN: (纸本)9781628415056

Percutaneous treatment of scaphoid fractures has found increasing interest in recent years as it promises to minimize soft-tissue damage, and minimizes the risk of infections and the loss of the joint stability. However, as this procedure is mostly performed on 2D fluoroscopic images, the accurate localization of the scaphoid bone for fracture fixation renders extremely challenging. In this work, we thus propose the integration of a statistical wrist model with 3D intraoperative ultrasound for accurate localization of the scaphoid bone. We utilize a previously developed statistical wrist model and register it to bone surfaces in ultrasound images using a probabilistic approach that involves expectation-maximization. We utilize local phase symmetry to detect features in noisy ultrasound images;in addition, we use shadow information in ultrasound images to enhance and set apart bone from other features. Feasibility experiments are performed by registering the wrist model to 3D ultrasound volumes of two different wrists at two different wrist positions. And the result indicates a potential of the proposed technique for localization of the scaphoid bone in ultrasound images.

关键词： Bone 3D modeling image registration Statistical analysis Ultrasonography Statistical modeling Clouds Surgery Navigation systems Injuries

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