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medical imaging 2014: image-guided procedures, robotic interventions, and modeling

ISBN: (纸本)9780819498298

The proceedings contain 98 papers. The topics discussed include: adaptive fiducial-free registration using multiple point selection for real-time electromagnetically navigated endoscopy;geometric estimation of intestinal contraction for motion tracking of video capsule endoscope;motion magnification for endoscopic surgery;reconstruction and feature selection for desorption electrospray ionization mass spectroscopy imagery;automatic standard plane adjustment on mobile C-Arm CT images of the calcaneus using atlas-based feature registration;mechanically assisted 3D ultrasound for pre-operative assessment and guiding percutaneous treatment of focal liver tumors;optoacoustic sensing for target detection inside cylindrical catheters;and polarization-sensitive multispectral tissue characterization for optimizing intestinal anastomosis.

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Hybrid Deep Learning Model for Robust Brain Tumor Detection: Integrating Filtering and Feature Extraction

Hybrid Deep Learning Model for Robust Brain Tumor Detection:...

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International conference on Trends in Electronics and Informatics (ICEI)

作者： N. Shyamala S. Mahaboob Basha Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical And Technical Sciences Saveetha University Chennai TamilNadu India

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

Brain tumor diagnosis remains a major obstacle in medical imaging since effective treatment planning requires precise identification and classification. Advanced filtering approaches are needed to improve the diagnostic accuracy of imaging modalities such as MRI (magnetic resonance imaging) and computed tomography (CT). Although deep learning has greatly increased the diagnostic accuracy of brain tumor recognition, problems with MRI image preprocessing still exist related to disturbances, relics, and low contrast. By including sophisticated filtering methods as the wavelet Transformation, which is Bilateral Filtration, and guided Filtering, which improves on conventional deep learning-based tumor categorization. To extract reliable tumor features, an integrated deep learning model that combines Efficient Net, the DenseNet and BiLSTM is proposed. With an accuracy of 97.5% as opposed to 90% in conventional CNN models, the results show enhanced segmentation efficiency, classification precision, and superior Grad-CAM visuals. The results show that the combination of sophisticated filters greatly enhances image quality, enabling more accurate tumor segmentation and classification. With the potential to improve diagnostic results offers insights into how these techniques might be incorporated into clinical procedures. Metrics like the Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR) are used to examine the filters. Despite retaining the structural details and image quality that are crucial to precise tumor diagnosis. These results show how important preprocessing and enhancement are to improve the diagnosis of brain tumors.

关键词： Deep learning image quality image segmentation Accuracy Filters Magnetic resonance imaging Brain tumors Bidirectional long short term memory Feature extraction Brain modeling

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Special Section Guest Editorial: Technology Platforms for Treatment and Discovery in Human Systems: Novel Work in image-guided procedures, robotic interventions, and modeling

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Journal of medical imaging 2018年第2期5卷 021201页

作者： Simpson, Amber Miga, Michael Memorial Sloan Kettering Cancer Center Department of Surgery New YorkNY United States Vanderbilt University Department of Biomedical Engineering NashvilleTN United States

This guest editorial introduces the Special Section on image-guided procedures, robotic interventions, and modeling in JMI Vol. 5 Issue 2.

关键词： Systems modeling robotics Surgery Data modeling medical imaging Cancer image registration Laparoscopy Tissues Ultrasonography

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medical robotics and Computer-Integrated Interventional Medicine

Medical Robotics and Computer-Integrated Interventional Medi...

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conference on medical imaging - image-guided procedures, robotic interventions and modeling

作者： Taylor, Russell H. Johns Hopkins Univ 3400 N Charles St Baltimore MD 21218 USA

ISBN: (纸本)9780819489654

Computer-Integrated Interventional Medicine (CIIM) promises to have a profound impact on health care in the next 20 years, much as and for many of the same reasons that the marriage of computers and information processing methods with other technology have had on manufacturing, transportation, and other sectors of our society. Our basic premise is that the steps of creating patient-specific computational models, using these models for planning, registering the models and plans with the actual patient in the operating room, and using this information with appropriate technology to assist in carrying out and monitoring the intervention are best viewed as part of a complete patient-specific intervention process that occurs over many time scales. Further, the information generated in computer-integrated interventions can be captured and analyzed statistically to improve treatment processes. This paper will explore these themes briefly, using examples drawn from our work at the Engineering Research Center for Computer-Integrated Surgical Systems and Technology (CISST ERC).

关键词： Computer-Integrated Surgery Computer-Integrated interventions medical robotics image-guided interventions medical imaging and modeling Experience Based Interventional Medicine

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image-based tracking of the suturing needle during laparoscopic interventions

Image-based tracking of the suturing needle during laparosco...

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conference on medical imaging - image-guided procedures, robotic interventions, and modeling

作者： Speidel, S. Kroehnert, A. Bodenstedt, S. Kenngott, H. Mueller-Stich, B. Dillmann, R. Karlsruhe Inst Technol Dept Informat Inst Anthropomat & Robot D-76021 Karlsruhe Germany Heidelberg Univ Dept Gen Abdominal & Transplantat Surg Heidelberg Germany

ISBN: (纸本)9781628415056

One of the most complex and difficult tasks for surgeons during minimally invasive interventions is suturing. A prerequisite to assist the suturing process is the tracking of the needle. The endoscopic images provide a rich source of information which can be used for needle tracking. In this paper, we present an image-based method for markerless needle tracking. The method uses a color-based and geometry-based segmentation to detect the needle. Once an initial needle detection is obtained, a region of interest enclosing the extracted needle contour is passed on to a reduced segmentation It is evaluated with in vivo images from da Vinci interventions.

关键词： Surgical Vision Localization & Tracking Technologies Endoscopic image Processing robotic-assisted surgery Laparoscopic procedures

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Integration of Fiber Optical Shape Sensing with medical Visualization for Minimal-Invasive interventions

Integration of Fiber Optical Shape Sensing with Medical Visu...

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conference on medical imaging - image-guided procedures, robotic interventions, and modeling

作者： Paetz, Torben Waltermann, Christian Angelmahr, Martin Ojdanic, Darko Schade, Wolfgang Witte, Michael Hahn, Horst Karl Fraunhofer MEVIS D-28359 Bremen Germany Fraunhoher HHI D-38640 Goslar Germany Fraunhoher HHI D-10587 Berlin Germany

ISBN: (纸本)9781628415056

We present a fiber optical shape sensing system that allows to track the shape of a standard telecom fiber with fiber Bragg grating. The shape sensing information is combined with a medical visualization platform to visualize the shape sensing information together with medical images and post-processing results like 3D models, vessel graphs, or segmentation results. The framework has a modular nature to use it for various medical applications like catheter or needle based interventions. The technology has potential in the medical area as it is MR-compatible and can easily be integrated in catheters and needles due to its small size.

关键词： FRG shape tracking medical visualization virtual secene

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image-guided procedures, robotic interventions, and modeling

Image-guided procedures, robotic interventions, and modeling

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medical imaging 2018: image-guided procedures, robotic interventions, and modeling

作者： Fei, Baowei Webster, Robert J.

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Toward requirement-based performance in data-driven image analysis algorithms for robotic applications

Toward requirement-based performance in data-driven image an...

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conference on medical imaging - image-guided procedures, robotic interventions, and modeling

作者： Hudak, Yuri F. Vrielink, Timo J. C. Oude van der Sommen, Fons Eindhoven Univ Technol NL-5612 AP Eindhoven Netherlands

ISBN: (纸本)9781510671614;9781510671607

Accurate and reliable medical image analysis, particularly in lung nodule segmentation, plays a crucial role in data-driven healthcare assistance technologies. Current evaluation metrics for segmentation algorithm performance, however, lack specificity to individual use cases and may not adequately assess the accuracy of 2D segmentation in context. In this preliminary work, we propose a novel evaluation approach that incorporates use case-specific evaluation metrics, focusing particularly on the spatial congruence and mass center accuracy of the nodule segmentation in the context of robot-assisted image-guided interventions. By simulating predicted segmentation masks using distortion techniques applied to ground truth masks from the LIDC-IDRI dataset, we compute the Dice score and Hausdorff distance, two common metrics for segmentation algorithm evaluation, as well as two proposed metrics, a Mass Center and a Centered Overlap score. Our preliminary findings indicate that the proposed metrics are superior to traditional ones, providing a more descriptive evaluation within the context of the intended use case. Future work will include comprehensive assessments of more extensive simulation techniques as well as the development and evaluation of a custom segmentation algorithm trained using our proposed metrics. By promoting the adoption of use case-specific metrics, we aim to improve the performance of segmentation algorithms, and ultimately, the outcome of critical healthcare procedures.

关键词： robotic interventions Segmentation Evaluation metrics System requirements

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A Fully Automatic image-to-world Registration Method for image-guided Procedure with Intraoperative imaging Updates

A Fully Automatic Image-to-world Registration Method for Ima...

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

作者： Li, Senhu Sarment, David Xoran Technol LLC Ann Arbor MI 48108 USA

ISBN: (纸本)9781510600218

image-guided procedure with intraoperative imaging updates has made a big impact on minimally invasive surgery. Compact and mobile CT imaging device combining with current commercial available image guided navigation system is a legitimate and cost-efficient solution for a typical operating room setup. However, the process of manual fiducial-based registration between image and physical spaces (image-to-world) is troublesome for surgeons during the procedure, which results in much procedure interruptions and is the main source of registration errors. In this study, we developed a novel method to eliminate the manual registration process. Instead of using probe to manually localize the fiducials during the surgery, a tracking plate with known fiducial positions relative to the reference coordinates is designed and fabricated through 3D printing technique. The workflow and feasibility of this method has been studied through a phantom experiment.

关键词： Surgical navigation image-to-world registration Intra-operative CT Target registration accuracy Automatic registration

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Transformation optimization and image blending for 3D liver ultrasound series stitching

Transformation optimization and image blending for 3D liver ...

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medical imaging conference - image-guided procedures, robotic interventions, and modeling

作者： Sun, Yuanyuan Kekec, Taygun Moelker, Adriaan Niessen, Wiro J. van Walsum, Theo Erasmus MC Biomed Imaging Grp Rotterdam Rotterdam Netherlands Delft Univ Technol Delft Netherlands

ISBN: (纸本)9781510633988

We propose a consistent ultrasound volume stitching framework, with the intention to produce a volume with higher image quality and extended field-of-view in this work. Directly using pair-wise registrations for stitching may lead to geometric errors. Therefore, we propose an approach to improve the image alignment by optimizing a consistency metric over multiple pairwise registrations. In the optimization, we utilize transformed points to effectively compute a distance between rigid transformations. The method has been evaluated on synthetic, phantom and clinical data. The results indicate that our transformation optimization method is effective and our stitching framework has a good geometric precision. Also, the compound images have been demonstrated to have improved CNR values.

关键词： 3D Ultrasound stitching Ultrasound guided intervention Liver

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