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

作者： Maekawa, Hinako Nakao, Megumi Mineura, Katsutaka Chen-Yoshikawa, Toyofumi F. Matsuda, Tetsuya Kyoto Univ Grad Sch Informat Kyoto Japan Kyoto Univ Hosp Dept Thorac Surg Kyoto Japan Nagoya Univ Hosp Dept Thorac Surg Nagoya Aichi Japan

ISBN: (纸本)9781510640269

In this study, we introduce statistical modeling methods for pneumothorax deformation using paired cone-beam computed tomography (CT) images. We designed a deformable mesh registration framework for shape changes involving non-linear deformation and rotation of the lungs. The registered meshes with local correspondences are available for both surgical guidance in thoracoscopic surgery and building statistical deformation models with inter-patient variations. In addition, a kernel-based deformation learning framework is proposed to reconstruct intraoperative deflated states of the lung from the preoperative CT models. This paper reports the findings of pneumothorax deformation and evaluation results of the kernel-based deformation framework.

关键词： Pneumothorax deformation Registration Statistical modeling Cone-beam CT Thoracoscopic Surgery

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Hyperspectral imaging for In-Vivo/Ex-Vivo Tissue Analysis of Human Brain Cancer

Hyperspectral Imaging for In-Vivo/Ex-Vivo Tissue Analysis of...

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

作者： Leon, Raquel Gelado, Sofia H. Fabelo, Himar Ortega, Samuel Quintana, Laura Szolna, Adam Pineiro, Juan F. Balea-Fernandez, Francisco Morera, Jesus Clavo, Bernardino Callico, Gustavo M. Univ Las Palmas Gran Canaria Res Inst Appl Microelect Las Palmas Gran Canaria Spain Univ Glasgow Dept Biomed Engn Glasgow Lanark Scotland Nofima Norwegian Inst Food Fisheries & Aquaculture Res Tromso Norway Univ Hosp Doctor Negrin Gran Canaria Dept Neurosurg Las Palmas Gran Canaria Spain Univ Las Palmas Gran Canaria Las Palmas Gran Canaria Spain Univ Hosp Doctor Negrin Gran Canaria Res Unit Las Palmas Gran Canaria Spain Inst Invest Sanitaria Canarias IISC Madrid Spain Res Inst Appl Microelect Las Palmas Gran Canaria Spain

ISBN: (纸本)9781510649446;9781510649439

Accurate identification of tumor boundaries during brain cancer surgery determines the quality of life of the patient. Different intraoperative guidance tools are currently employed during the resection tumor but having several limitations. Hyperspectral imaging (HSI) is arising as a label-free and non-ionizing technique that could assist neurosurgeons during surgical procedures. In this paper, an analysis between in-vivo and ex-vivo human brain tumor samples using HSI has been performed to evaluate the correlation between both types of samples. Spectral ratios of the oxygenated and deoxygenated hemoglobin were employed to distinguish between normal tissue, tumor tissue and blood vessels. A database composed by seven in-vivo and fourteen ex-vivo hyperspectral images obtained from seven different patients diagnosed with glioblastoma Grade IV, metastatic secondary breast cancer, meningioma Grade I and II, and astrocytoma (glioma) Grade II. 44,964 pixels labeled pixels were employed in this work. The proposed method achieved discrimination between different tissue types using the proposed spectral ratio. Comparison between in-vivo and ex-vivo samples indicated that ex-vivo samples generate higher hemoglobin ratios. Moreover, vascular enhanced maps were generated using the spectral ratio, targeting real-time intraoperative surgical assistance.

关键词： Brain tumor cancer surgery hyperspectral imaging intraoperative imaging hemoglobin ratio

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imaging-compatible oral retractor system for use in image-guided transoral robotic surgery

Imaging-compatible oral retractor system for use in image-gu...

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

作者： Shi, Yuan Wu, Xiaotian Paydarfar, Joseph A. Halter, Ryan J. Dartmouth Coll Thayer Sch Engn Hanover NH 03755 USA Harvard Med Sch Massachusetts Gen Hosp Gordon Ctr Med Imaging Boston MA 02115 USA Dartmouth Hitchcock Med Ctr Sect Otolaryngol Audiol & Maxillofacial Surg Dept Surg Lebanon NH 03766 USA Dartmouth Coll Geisel Sch Med Hanover NH 03755 USA

ISBN: (纸本)9781510640269

Currently-available metallic retractors typically used in transoral robotic surgery (TORS) cause significant artifacts in CT imaging and cannot be safely used in MRI. The lack of imaging-compatible oral retractors poses a significant challenge to enabling intraoperative imaging in TORS. This work introduces a customizable compact 3D-printed polymer retractor system that enables multiple modes of adjustability, artifact-free CT and MR images, and adequate surgical exposure. The polymer retractor design was modeled after the traditional metal FK and Crowe-Davis retractors and can be used with an acrylic suspension system that rests over the patient's chest. Finite element analysis was conducted to evaluate the mechanical performance in relevant clinical loading conditions. Cadaver experiments followed by endoscopic, CT, and MR imaging were performed to demonstrate functionality. Artifact-free CT and MR images were obtained. An inter-incisive distance of 42.50 mm and 200.09 cm(3) working volume were achieved, which allow the introduction of robotic arms and necessary instruments in TORS. This polymer retractor system makes it possible to acquire intraoperative images and establishes a critical step to make image-guided TORS both feasible and effective.

关键词： transoral surgery CT/MR-compatible retractor 3D printing image guidance intraoperative imaging

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Inpainting surgical occlusion from laparoscopic video sequences for robot-assisted interventions

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JOURNAL OF medical imaging 2023年第4期10卷 045002页

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

Purpose medical technology for minimally invasive surgery has undergone a paradigm shift with the introduction of robot-assisted surgery. However, it is very difficult to track the position of the surgical tools in a surgical scene, so it is crucial to accurately detect and identify surgical tools. This task can be aided by deep learning-based semantic segmentation of surgical video frames. Furthermore, due to the limited working and viewing areas of these surgical instruments, there is a higher chance of complications from tissue injuries (e.g., tissue scars and tears).Approach With the aid of digital inpainting algorithms, we present an application that uses image segmentation to remove surgical instruments from laparoscopic/endoscopic video. We employ a modified U-Net architecture (U-NetPlus) to segment the surgical instruments. It consists of a redesigned decoder and a pre-trained VGG11 or VGG16 encoder. The decoder was modified by substituting an up-sampling operation based on nearest-neighbor interpolation for the transposed convolution operation. Furthermore, these interpolation weights do not need to be learned to perform upsampling, which eliminates the artifacts generated by the transposed convolution. In addition, we use a very fast and adaptable data augmentation technique to further enhance performance. The instrument segmentation mask is filled in (i.e., inpainted) by the tool removal algorithms using the previously acquired tool segmentation masks and either previous instrument-containing frames or instrument-free reference *** We have shown the effectiveness of the proposed surgical tool segmentation/removal algorithms on a robotic instrument dataset from the MICCAI 2015 and 2017 EndoVis Challenge. We report a 90.20% DICE for binary segmentation, a 76.26% DICE for instrument part segmentation, and a 46.07% DICE for instrument type (i.e., all instruments) segmentation on the MICCAI 2017 challenge dataset using our U-NetPlus architecture, ou

关键词： inpainting segmentation deep learning computer vision poisson blending optical-flow

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Effect of Uncertainty on Target Registration Error in image-guided Renal interventions: From Simulation to in-vitro Assessment

Effect of Uncertainty on Target Registration Error in Image-...

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

作者： Jackson, Peter Simon, Richard Linte, Cristian Rochester Inst Technol Ctr Imaging Sci Rochester NY 14623 USA Rochester Inst Technol Dept Biomed Engn Rochester NY 14623 USA

ISBN: (纸本)9781510640269

It is imperative that image-guided intervention (IGI) systems provide accurate and precise navigation information to enable the user to trust the system and not place unwarranted confidence in the guidance capabilities of the system. Unfortunately, the actual error associated with the overall targeting capabilities of an IGI system is not readily known. Here we are primarily interested in the application of image-guided surgery in the context of renal interventions. We built a simulation pipeline to study the uncertainty propagation through an optically tracked IGI system to gain insight into the overall accuracy of the system. Our simulation pipeline models several stages, including stylus calibration, tool tracking, patient tracking, and image to patient registration. In the effort to realistically estimate tracking noise and user-associated fiducial localization error (FLE), we conducted several experiments using the optical tracking system. Our simulation suggested that a wider cone angle results in a more accurate tool calibration, which improves further with the collection of additional samples. Furthermore, our simulations also suggested that the image-to-patient registration was the most significant contributor to navigation uncertainty, followed by the fiducial localization error. Lastly, we also observed a 0.72 correlation between the Target Registration Error (TRE) estimated at target fiducials and the distance between the the centroids of the registration and target fiducial landmarks. To validate the simulation predictions, we also conducted several in vitro experiments using a 3D printed patient specific kidney phantom and compared the simulation-based registration predictions with those observed experimentally in vitro. The experiments confirmed the registration metrics (Fiducial Registration Error and TRE) predicted by the simulations, given several specific combinations of fiducial landmarks used to perform the image to patient registration.

关键词： image-guided Navigation Error Analysis Simulation Registration

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Automatic microchannel detection using deep learning in intravascular optical coherence tomography images

Automatic microchannel detection using deep learning in intr...

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

作者： Lee, Juhwan Kim, Justin N. Pereira, Gabriel T. R. Gharaibeh, Yazan Kolluru, Chaitanya Zimin, Vladislav N. Dallan, Luis A. P. Motairek, Issam K. Hoori, Ammar Guagliumi, Giulio Bezerra, Hiram G. Wilson, David L. Case Western Reserve Univ Dept Biomed Engn Cleveland OH 44106 USA Univ Hosp Cleveland Harrington Heart & Vasc Inst Med Ctr Cardiovasc Imaging Core Lab Cleveland OH 44106 USA Osped Papa Giovanni XXIII Cardiovasc Dept Bergamo Italy Univ S Florida Intervent Cardiol Ctr Inst Heart & Vasc Tampa FL 33606 USA Case Western Reserve Univ Dept Radiol Cleveland OH 44106 USA

ISBN: (纸本)9781510649446;9781510649439

Microchannel formation is known to be a significant marker of plaque vulnerability, plaque rupture, and intraplaque hemorrhage, which are responsible for plaque progression. We developed a fully-automated method for detecting microchannels in intravascular optical coherence tomography (IVOCT) images using deep learning. A total of 3,075 IVOCT image frames across 41 patients having 62 microchannel segments were analyzed. Microchannel was manually annotated by expert cardiologists, according to previously established criteria. In order to improve segmentation performance, pre-processing including guidewire detection/removal, lumen segmentation, pixel-shifting, and noise filtering was applied to the raw (r,theta) IVOCT image. We used the DeepLab-v3 plus deep learning model with the Xception backbone network for identifying microchannel candidates. After microchannel candidate detection, each candidate was classified as either microchannel or no-microchannel using a convolutional neural network (CNN) classification model. Our method provided excellent segmentation of microchannel with a Dice coefficient of 0.811, sensitivity of 92.4%, and specificity of 99.9%. We found that pre-processing and data augmentation were very important to improve results. In addition, a CNN classification step was also helpful to rule out false positives. Furthermore, automated analysis missed only 3% of frames having microchannels and showed no false positives. Our method has great potential to enable highly automated, objective, repeatable, and comprehensive evaluations of vulnerable plaques and treatments. We believe that this method is promising for both research and clinical applications.

关键词： Intravascular optical coherence tomography microchannel deep learning segmentation classification

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Assessment of proton beam ablation in myocardial infarct tissue using delayed contrast-enhanced magnetic resonance imaging

Assessment of proton beam ablation in myocardial infarct tis...

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

作者： Rettmann, M. E. Hohmann, S. Deisher, A. J. Konishi, H. Kruse, J. J. Newman, L. K. Parker, K. D. Herman, M. G. Packer, D. L. Mayo Clin Div Cardiovasc Dis Rochester MN 55901 USA Mayo Clin Dept Radiat Oncol Rochester MN USA

ISBN: (纸本)9781510633988

External beam therapy has recently been proposed for treatment of ventricular tachycardia (VT). One of the goals of VT ablation is to eliminate myocardial channels by homogenizing infarct scar, thereby inhibiting reentry and eliminating VT. It has been previously shown that proton beam therapy can create lesions in healthy myocardial tissue, thereby suggesting a potential for treatment of VT. To date;however, the effects of proton beam energy in infarcted myocardial tissue is unknown. In the current work, we track proton beam dose maps in a series of delayed contrast-enhanced magnetic resonance imaging swine datasets across 16 weeks to assess the effects of proton beam ablation on infarcted myocardial tissue.

关键词： Magnetic resonance imaging Computed tomography image segmentation image registration Visualization In vivo imaging Radiation oncology

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image registration between MRI and spot mammograms for X-ray guided stereotactic breast biopsy: preliminary results

Image registration between MRI and spot mammograms for X-ray...

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

作者： Said, S. Clauser, P. Ruiter, N., V Baltzer, P. A. T. Hopp, T. Karlsruhe Inst Technol KIT Inst Data Proc & Elect Karlsruhe Germany Med Univ Vienna Dept Biomed Imaging & Image Guided Therapy Vienna Austria

ISBN: (纸本)9781510640269

Breast cancer is the most common cancer type among women. Approximately 40,000 women are expected to die from breast cancer every year. While digital mammography has a central role in the early diagnosis of breast cancer, many cancers are not visible in mammography, for example in women with dense breast tissue. Contrast enhanced magnetic resonance imaging (CE-MRI) of the breast is often used to detect lesions not visible in mammography. Lesions with suspicious characteristics on CE-MRI need to be further assessed with MRI-guided biopsy. However, MRI-guided biopsy is expensive, time consuming, and not widely available. In this paper, a novel method for a matching tool between MRI and spot mammograms is proposed. Our aim is to transfer information that is only visible in MRI onto mammographic spot projections, to enable X-ray guided biopsy even if the lesion is only visible in MRI. Two methods of registration in combination are used;a biomechanical model based registration between MRI and full view X-ray mammograms and a subsequent image based registration between full mammograms and spot mammograms. Preliminary results assessed for one patient from the medical University of Vienna are presented. The target registration error (TRE) of biomechanical model based registration is 2.4 mm and the TRE of the image based registration is 9.5 mm. The total TRE of the two steps is 7.3 mm.

关键词： Biomechanical Model Based Registration image Based Registration Magnetic Resonance imaging Full Mammograms Spot Mammograms

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Multi-body statistical shape representation of anatomy for navigation in Robot-Assisted Laparoscopic Partial Nephrectomy

Multi-body statistical shape representation of anatomy for n...

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

作者： Kokko, Michael A. Seigne, John D. Van Citters, Douglas W. Halter, Ryan J. Dartmouth Coll Thayer Sch Engn 14 Engn Dr Hanover NH 03755 USA Dartmouth Coll Geisel Sch Med 1 Rope Ferry Rd Hanover NH 03755 USA Dartmouth Hitchcock Med Ctr Div Urol 1 Med Ctr Dr Lebanon NH 03756 USA

ISBN: (纸本)9781510640269

image guidance for abdominal procedures requires an anatomical model capable of representing significant displacement and deformation of relevant tissues in a computationally efficient manner. This work evaluates the suitability of statistical shape modeling to represent key structures in Robot Assisted Laparoscopic Partial Nephrectomy (RALPN) both individually, and also as a multi-body composite model. Tomography obtained from subjects in an ongoing RALPN study was used to produce surface model representations of the kidneys, abdominal aorta, and inferior vena cava. Each structure was resliced and remeshed in a standardized fashion to allow for extraction of the principal modes of variation. Reduced parameter representations of the example structures based on the strongest eigenmodes indicate that <5mm average RMSE modeling accuracy can be achieved with four parameters for the individual models and eight parameters for the four-body composite model. The magnitude of centroid displacements observed under the principal modes of variation is consistent with literature-reported values, suggesting that this approach may be suitable for image guidance in RALPN.

关键词： Statistical Shape modeling Partial Nephrectomy Anatomical modeling Tissue Displacement Tissue Deformation

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Feasibility of Robot-Assisted Ultrasound imaging with Force Feedback for Assessment of Thyroid Diseases

Feasibility of Robot-Assisted Ultrasound Imaging with Force ...

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

作者： Kaminski, Jakub T. Rafatzand, Khashayar Zhang, Haichong K. Worcester Polytech Inst Robot Engn Program Worcester MA 01609 USA Worcester Polytech Inst Dept Biomed Engn Worcester MA 01609 USA Univ Massachusetts Sch Med Dept Radiol Amherst MA 01003 USA

ISBN: (纸本)9781510633988

medical ultrasound is extensively used to define tissue textures and to characterize lesions, and it is the modality of choice for detection and follow-up assessment of thyroid diseases. Classical medical ultrasound procedures are performed manually by an occupational operator with a hand-held ultrasound probe. These procedures require high physical and cognitive burden and yield clinical results that are highly operator-dependent, therefore frequently diminishing trust in ultrasound imaging data accuracy in repetitive assessment. A robotic ultrasound procedure, on the other hand, is an emerging paradigm integrating a robotic arm with an ultrasound probe. It achieves an automated or semi-automated ultrasound scanning by controlling the scanning trajectory, region of interest, and the contact force. Therefore, the scanning becomes more informative and comparable in subsequent examinations over a long-time span. In this work, we present a technique for allowing operators to reproduce reliably comparable ultrasound images with the combination of predefined trajectory execution and real-time force feedback control. The platform utilized features a 7-axis robotic arm capable of 6-DoF force-torque sensing and a linear-array ultrasound probe. The measured forces and torques affecting the probe are used to adaptively modify the predefined trajectory during autonomously performed examinations and probe-phantom interaction force accuracy is evaluated. In parallel, by processing and combining ultrasound B-Mode images with probe spatial information, structural features can be extracted from the scanning volume through a 3D scan. The validation was performed on a tissue-mimicking phantom containing thyroid features, and we successfully demonstrated high image registration accuracy between multiple trials.

关键词： robotic ultrasound 3D ultrasound ultrasound image registration force control motion planning 3D imaging

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