检索结果-内蒙古大学图书馆

Longitudinal Analysis of Disease Progression Using Image and laboratory Data for Covid-19 Patients

学校读者我要写书评

暂无评论

Longitudinal Analysis of Disease Progression Using Image and...

German Workshop on medical Image Computing, 2022

作者： De Benetti, Francesca Bentele, Verena Burian, Egon Makowski, Marcus Navab, Nassir Braren, Rickmer Wendler, Thomas Chair for Computer Aided Medical Procedures and Augmented Reality TU Munich München Germany Department of Diagnostic and Interventional Radiology TU Munich München Germany Department of Diagnostic and Interventional Neuroradiology TU Munich München Germany Computer Aided Medical Procedures Lab Laboratory for Computational Sensing+Robotics Johns Hopkins University Baltimore United States

Thoracic Cartilage Ultrasound-CT Registration using Dense Skeleton Graph

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Jiang, Zhongliang Li, Chenyang Li, Xuesong Navab, Nassir Chair for Computer Aided Medical Procedures and Augmented Reality Technical University of Munich Germany Laboratory for Computational Sensing and Robotics Johns Hopkins University BaltimoreMD United States

Autonomous ultrasound (US) imaging has gained increased interest recently, and it has been seen as a potential solution to overcome the limitations of free-hand US examinations, such as inter-operator variations. However, it is still challenging to accurately map planned paths from a generic atlas to individual patients, particularly for thoracic applications with high acoustic-impedance bone structures under the skin. To address this challenge, a graph-based non-rigid registration is proposed to enable transferring planned paths from the atlas to the current setup by explicitly considering subcutaneous bone surface features instead of the skin surface. To this end, the sternum and cartilage branches are segmented using a template matching to assist coarse alignment of US and CT point clouds. Afterward, a directed graph is generated based on the CT template. Then, the self-organizing map using geographical distance is successively performed twice to extract the optimal graph representations for CT and US point clouds, individually. To evaluate the proposed approach, five cartilage point clouds from distinct patients are employed. The results demonstrate that the proposed graph-based registration can effectively map trajectories from CT to the current setup for displaying US views through limited intercostal space. The non-rigid registration results in terms of Hausdorff distance (Mean±SD) is 9.48 ±0.27 mm and the path transferring error in terms of Euclidean distance is 2.21 ± 1.11 mm. Copyright © 2023, The Authors. All rights reserved.

关键词： Acoustic impedance

OCT-guided Robotic Subretinal Needle Injections: A Deep Learning-Based Registration Approach

学校读者我要写书评

暂无评论

OCT-guided Robotic Subretinal Needle Injections: A Deep Lear...

2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Mach, Kristina Wei, Shuwen Kim, Ji Woong Martin-Gomez, Alejandro Zhang, Peiyao Kang, Jin U. Nasseri, M. Ali Gehlbach, Peter Navab, Nassir Iordachita, Iulian Johns Hopkins University Laboratory for Computational Sensing and Robotics Baltimore United States Klinikum Rechts der Isar der Technische Universität Augenklinik und Poliklinik Munich Germany Wilmer Eye Institute Johns Hopkins Hospital Baltimore United States Technical University of Munich Chair for Computer Aided Medical Procedures Germany

ISBN: (纸本)9781665468190

Subretinal injection (SI) is an ophthalmic surgical procedure that allows for the direct injection of therapeutic substances into the subretinal space to treat vitreoretinal disorders. Although this treatment has grown in popularity, various factors contribute to its difficulty. These include the retina's fragile, nonregenerative tissue, as well as hand tremor and poor visual depth perception. In this context, the usage of robotic devices may reduce hand tremors and facilitate gradual and controlled SI. For the robot to successfully move to the target area, it needs to understand the spatial relationship between the attached needle and the tissue. The development of optical coherence tomography (OCT) imaging has resulted in a substantial advancement in visualizing retinal structures at micron resolution. This paper introduces a novel foundation for an OCT-guided robotic steering framework that enables a surgeon to plan and select targets within the OCT volume. At the same time, the robot automatically executes the trajectories necessary to achieve the selected targets. Our contribution consists of a novel combination of existing methods, creating an intraoperative OCT-Robot registration pipeline. We combined straightforward affine transformation computations with robot kinematics and a deep neural network-determined tool-tip location in OCT. We evaluate our framework's capability in a cadaveric pig eye open-sky procedure and using an aluminum target board. Targeting the subretinal space of the pig eye produced encouraging results with a mean Euclidean error of 23.8μm. © 2022 IEEE.

关键词： Optical tomography

AutoPaint: A Self-Inpainting Method for Unsupervised Anomaly Detection

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Astaraki, Mehdi De Benetti, Francesca Yeganeh, Yousef Toma-Dasu, Iuliana Smedby, Örjan Wang, Chunliang Navab, Nassir Wendler, Thomas KTH Royal Institute of Technology Department of Biomedical Engineering and Health Systems HuddingeSE-14157 Sweden Karolinska Institutet Department of Oncology-Pathology StockholmSE-17176 Sweden Chair for Computer Aided Medical Procedures and Augmented Reality Technische Universitat München Boltzmannstr. 3 Garching bei München85748 Germany Stockholm University Department of Physics StockholmSE-106 91 Sweden Chair for Computer Aided Medical Procedures Laboratory for Computational Sensing and Robotics Johns-Hopkins University BaltimoreMD United States SurgicEye GmbH Munich Germany

Robust and accurate detection and segmentation of heterogenous tumors appearing in different anatomical organs with supervised methods require large-scale labeled datasets covering all possible types of diseases. Due to the unavailability of such rich datasets and the high cost of annotations, unsupervised anomaly detection (UAD) methods have been developed aiming to detect the pathologies as deviation from the normality by utilizing the unlabeled healthy image data. However, developed UAD models are often trained with an incomplete distribution of healthy anatomies and have difficulties in preserving anatomical constraints. This work intends to, first, propose a robust inpainting model to learn the details of healthy anatomies and reconstruct high-resolution images by preserving anatomical constraints. Second, we propose an autoinpainting pipeline to automatically detect tumors, replace their appearance with the learned healthy anatomies, and based on that segment the tumoral volumes in a purely unsupervised fashion. Three imaging datasets, including PET, CT, and PET-CT scans of lung tumors and head and neck tumors, are studied as benchmarks for evaluation. Experimental results demonstrate the significant superiority of the proposed method over a wide range of state-of-the-art UAD methods. Moreover, the unsupervised method we propose produces comparable results to a robust supervised segmentation method when applied to multimodal images. Copyright © 2023, The Authors. All rights reserved.

关键词： Tumors

Complementary Textures. A Novel Approach to Object Alignment in Mixed Reality

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Martin-Gomez, Alejandro Winkler, Alexander de la Tijera Obert, Rafael Fotouhi, Javad Roth, Daniel Eck, Ulrich Navab, Nassir The Laboratory for Computational Sensing and Robotics Johns Hopkins University United States The Chair for Computer Aided Medical Procedures Technical University of Munich Germany Philips Research North America United States The Human-Centered Computing and Extended Reality Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Germany

Alignment between real and virtual objects is a challenging task required for the deployment of Mixed Reality (MR) into manufacturing, medical, and construction applications. To face this challenge, a series of methods have been proposed. While many approaches use dynamic augmentations such as animations, arrows, or text to assist users, they require tracking the position of the real objects. In contrast, when tracking of the real objects is not available or desired, alternative approaches use virtual replicas of real objects to allow for interactive, perceptual virtual-to-real and/or real-to-virtual alignment. In these cases, the accuracy achieved strongly depends on the quality of the perceptual information provided to the user. This paper proposes a novel set of perceptual alignment concepts that go beyond the use of traditional visualization of the virtual replicas, introducing the concept of COMPLEMENTARY TEXTURES to improve interactive alignment in MR applications. To showcase the advantages of using COMPLEMENTARY TEXTURES, we describe three different implementations that provide highly salient visual cues when misalignment is observed;or present semantic augmentations that, when combined with a real object, provide contextual information that can be used during the alignment process. The authors aim at opening new paths for the community to explore rather than describing end-to-end solutions. The objective is to show multitude of opportunities such concepts could provide for further research and development. © 2022, CC BY.

关键词： Augmented reality

OCT-guided Robotic Subretinal Needle Injections: A Deep Learning-Based Registration Approach

学校读者我要写书评

暂无评论

OCT-guided Robotic Subretinal Needle Injections: A Deep Lear...

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Kristina Mach Shuwen Wei Ji Woong Kim Alejandro Martin-Gomez Peiyao Zhang Jin U. Kang M. Ali Nasseri Peter Gehlbach Nassir Navab Iulian Iordachita Laboratory for Computational Sensing and Robotics Johns Hopkins University Baltimore USA Augenklinik und Poliklinik Klinikum rechts der Isar der Technische Universität Munich Germany Johns Hopkins Hospital Wilmer Eye Institute Baltimore USA Chair for Computer Aided Medical Procedures Technical University of Munich Germany

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Subretinal injection (SI) is an ophthalmic surgical procedure that allows for the direct injection of therapeutic substances into the subretinal space to treat vitreoretinal disorders. Although this treatment has grown in popularity, various factors contribute to its difficulty. These include the retina’s fragile, nonregenerative tissue, as well as hand tremor and poor visual depth perception. In this context, the usage of robotic devices may reduce hand tremors and facilitate gradual and controlled SI. For the robot to successfully move to the target area, it needs to understand the spatial relationship between the attached needle and the tissue. The development of optical coherence tomography (OCT) imaging has resulted in a substantial advancement in visualizing retinal structures at micron resolution. This paper introduces a novel foundation for an OCT-guided robotic steering framework that enables a surgeon to plan and select targets within the OCT volume. At the same time, the robot automatically executes the trajectories necessary to achieve the selected targets. Our contribution consists of a novel combination of existing methods, creating an intraoperative OCT-Robot registration pipeline. We combined straightforward affine transformation computations with robot kinematics and a deep neural network-determined tool-tip location in OCT. We evaluate our framework’s capability in a cadaveric pig eye open-sky procedure and using an aluminum target board. Targeting the subretinal space of the pig eye produced encouraging results with a mean Euclidean error of 23.8μm.

关键词： Visualization Robot kinematics Pipelines Aluminum Surgery Needles Retina

Exploring the Needle Tip Interaction Force with Retinal Tissue Deformation in Vitreoretinal Surgery

学校读者我要写书评

暂无评论

Exploring the Needle Tip Interaction Force with Retinal Tiss...

IEEE International Conference on robotics and Automation (ICRA)

作者： Simon Pannek Shervin Dehghani Michael Sommersperger Peiyao Zhang Peter Gehlbach M. Ali Nasseri Iulian Iordachita Nassir Navab Department of Computer Science Technische Universität München München Germany Laboratory for Computational Sensing and Robotics Johns Hopkins University Baltimore MD USA Wilmer Eye Institute Johns Hopkins Hospital Baltimore MD USA Augenklinik und Poliklinik Klinikum Rechts der Isar der Technische Universität München München Germany Professor and the Chair for Computer Aided Medical Procedures & Augmented Reality Technical University of Munich Munich Germany Adjunct Professor at the Whiting School of Engineering Johns Hopkins University Baltimore MD USA

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Recent advancements in age-related macular degeneration treatments necessitate precision delivery into the subretinal space, emphasizing minimally invasive procedures targeting the retinal pigment epithelium (RPE)-Bruch’s membrane complex without causing trauma. Even for skilled surgeons, the inherent hand tremors during manual surgery can jeopardize the safety of these critical *** has fostered the evolution of robotic systems designed to prevent such tremors. These robots are enhanced by FBG sensors, which sense the small force interactions between the surgical instruments and retinal tissue. To enable the community to design algorithms taking advantage of such force feedback data, this paper focuses on the need to provide a specialized dataset, integrating optical coherence tomography (OCT) imaging together with the aforementioned force *** introduce a unique dataset, integrating force sensing data synchronized with OCT B-scan images, derived from a sophisticated setup involving robotic assistance and OCT integrated microscopes. Furthermore, we present a neural network model for image-based force estimation to demonstrate the dataset’s applicability.

关键词： Force Estimation Pigments Robot sensing systems Retina Needles Robot learning

Deformation-aware robotic 3D ultrasound

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Jiang, Zhongliang Zhou, Yue Bi, Yuan Zhou, Mingchuan Wendler, Thomas Navab, Nassir Chair for Computer Aided Medical Procedures and Augmented Reality Technical University of Munich Germany College of Biosystems of Engineering and Food Science Zhejiang University China Laboratory for Computational Sensing and Robotics Johns Hopkins University Baltimore United States

Tissue deformation in ultrasound (US) imaging leads to geometrical errors when measuring tissues due to the pressure exerted by probes. Such deformation has an even larger effect on 3D US volumes as the correct compounding is limited by the inconsistent location and geometry. This work proposes a patient-specified stiffness-based method to correct the tissue deformations in robotic 3D US acquisitions. To obtain the patient-specified model, robotic palpation is performed at sampling positions on the tissue. The contact force, US images and the probe poses of the palpation procedure are recorded. The contact force and the probe poses are used to estimate the nonlinear tissue stiffness. The images are fed to an optical flow algorithm to compute the pixel displacement. Then the pixel-wise tissue deformation under different forces is characterized by a coupled quadratic regression. To correct the deformation at unseen positions on the trajectory for building 3D volumes, an interpolation is performed based on the stiffness values computed at the sampling positions. With the stiffness and recorded force, the tissue displacement could be corrected. The method was validated on two blood vessel phantoms with different stiffness. The results demonstrate that the method can effectively correct the force-induced deformation and finally generate 3D tissue geometries. Copyright © 2021, The Authors. All rights reserved.

关键词： Deformation

Weakly-supervised Biomechanically-constrained CT/MRI Registration of the Spine

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Jian, Bailiang Azampour, Mohammad Farid De Benetti, Francesca Oberreuter, Johannes Bukas, Christina Gersing, Alexandra S. Foreman, Sarah C. Dietrich, Anna-Sophia Rischewski, Jon Kirschke, Jan S. Navab, Nassir Wendler, Thomas Chair for Computer Aided Medical Procedures and Augmented Reality Technische Universität München Garching Germany ScintHealth GmbH Munich Germany Reply SpA Munich Germany Helmholtz AI Helmholtz Zentrum München Munich Germany Department of Radiology Technische Universität München Munich Germany Department of Neuroradiology Technische Universität München Munich Germany Computer Aided Medical Procedures Lab Laboratory for Computational Sensing+Robotics Johns Hopkins University BaltimoreMD United States

Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are two of the most informative modalities in spinal diagnostics and treatment planning. CT is useful when analysing bony structures, while MRI gives information about the soft tissue. Thus, fusing the information of both modalities can be very beneficial. Registration is the first step for this fusion. While the soft tissues around the vertebra are deformable, each vertebral body is constrained to move rigidly. We propose a weakly-supervised deep learning framework that preserves the rigidity and the volume of each vertebra while maximizing the accuracy of the registration. To achieve this goal, we introduce anatomy-aware losses for training the network. We specifically design these losses to depend only on the CT label maps since automatic vertebra segmentation in CT gives more accurate results contrary to MRI. We evaluate our method on an in-house dataset of 167 patients. Our results show that adding the anatomy-aware losses increases the plausibility of the inferred transformation while keeping the accuracy untouched. Copyright © 2022, The Authors. All rights reserved.

关键词： Magnetic resonance imaging