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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

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

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

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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

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Thoracic Cartilage Ultrasound-CT Registration Using Dense Skeleton Graph

Thoracic Cartilage Ultrasound-CT Registration Using Dense Sk...

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IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Zhongliang Jiang Chenyang Li Xuesong Lil Nassir Navab Chair for Computer Aided Medical Procedures and Augmented Reality Technical University of Munich Germany Laboratory for Computational Sensing and Robotics Johns Hopkins University Baltimore MD USA

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 exami-nations, 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 below the skin. To address this challenge, a dense graph-based non-rigid registration is proposed to transfer planned paths from the atlas to the current setup by explicitly considering subcutaneous bone 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 to do US examination through limited intercostal space. The non-rigid registration results in terms of Hausdorff distance (Mean±SD) is $9.48 \pm 0.27$ mm and the path transferring error in terms of Euclidean distance is $2.21\pm 1.11\ mm$ . The code 1 1 https://***/marslicy/Cartilage-graph-based-US-CT-Registration and video 2 2 Video: https://***/watch?v=QJz2fkwgbP8 can be publicly accessed.

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Thoracic Cartilage Ultrasound-CT Registration using Dense Skeleton Graph

arXiv

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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

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Deformation-aware robotic 3D ultrasound

arXiv

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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

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Exploiting partial structural symmetry for patient-specific image augmentation in trauma interventions

arXiv

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arXiv 2018年

作者： Fotouhi, Javad Unberath, Mathias Taylor, Giacomo Farashahi, Arash Ghaani Bier, Bastian Taylor, Russell H. Osgood, Greg M. Armand, Mehran Navab, Nassir Computer Aided Medical Procedures Johns Hopkins University Department of Mechanical Engineering Johns Hopkins University Laboratory for Computational Sensing and Robotics Johns Hopkins University Department of Orthopaedic Surgery Johns Hopkins Hospital Applied Physics Laboratory Johns Hopkins University Computer Aided Medical Procedures Technische Universität München

In unilateral pelvic fracture reductions, surgeons attempt to reconstruct the bone fragments such that bilateral symmetry in the bony anatomy is restored. We propose to exploit this"structurally symmetric" nature of the pelvic bone, and provide intra-operative image augmentation to assist the surgeon in repairing dislocated fragments. The main challenge is to automatically estimate the desired plane of symmetry within the patient’s pre-operative CT. We propose to estimate this plane using a non-linear optimization strategy, by minimizing Tukey’s biweight robust estimator, relying on the partial symmetry of the anatomy. Moreover, a regularization term is designed to enforce the similarity of bone density histograms on both sides of this plane, relying on the biological fact that, even if injured, the dislocated bone segments remain within the body. The experimental results demonstrate the performance of the proposed method in estimating this"plane of partial symmetry" using CT images of both healthy and injured anatomy. Examples of unilateral pelvic fractures are used to show how intra-operative X-ray images could be augmented with the forward-projections of the mirrored anatomy, acting as objective road-map for fracture reduction procedures. Copyright © 2018, The Authors. All rights reserved.

关键词： computerized tomography

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Collaboration analysis using deep learning

arXiv

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arXiv 2019年

作者： Guo, Zhang Yu, Kevin Pearlman, Rebecca Navab, Nassir Barmaki, Roghayeh Department of Computer and Information Sciences University of Delaware Chair for Computer Aided Medical Procedures and Augmented Reality Technical University of Munich Department of Biology Krieger School of Arts and Sciences Johns Hopkins University Laboratory of Computational Sensing and Robotics Johns Hopkins University

The analyses of collaborative learning process is one of the growing field of education research, which has many different analytic solutions. In this paper, we provided a new solution to improve the automated collaborative learning analyses using deep neural networks. Instead of using selfreported questionnaires, which are subject to bias and noise, we automatically extract group-working information by object recognition results using Mask R-CNN method. This process is based on detecting the people and other objects from pictures and video clips of the collaborative learning process, then evaluate the mobile learning performance using the collaborative indicators. We tested our approach to automatically evaluate the group-work collaboration in a controlled study of 33 dyads while performing an anatomy body painting intervention. The results indicate that our approach recognizes the differences of collaborations among teams of treatment and control groups in the case study;F(1, 32)= 4.83, p MSC Codes 68U10 Copyright © 2019, The Authors. All rights reserved.

关键词： Object recognition

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Robotic ultrasound trajectory planning for volume of interest coverage

Robotic ultrasound trajectory planning for volume of interes...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Christoph Graumann Bernhard Fuerst Christoph Hennersperger Felix Bork Nassir Navab Laboratory for Computational Sensing and Robotics The Johns Hopkins University Baltimore MD USA Computer Aided Medical Procedures and Augmented Reality Technische Universität München Garching bei München Germany

medical robotic ultrasound offers potential to assist interventions, ease long-term monitoring and reduce operator dependency. Various techniques for remote control of ultrasound probes through telemanipulation systems have been presented in the past, however not exploiting the potential of fully autonomous acquisitions directly performed by robotic systems. In this paper, a trajectory planning algorithm for automatic robotic ultrasound acquisition under expert supervision is introduced. The objective is to compute a suitable path for covering a volume of interest selected in diagnostic images, for example by prior segmentation. A 3D patient surface point cloud is acquired using a depth camera, which is the sole prerequisite besides the volume delineation. An easily parameterizable path function generates single or multiple parallel scan trajectories capable of dealing with large target volumes. A spline is generated through the preliminary path points and is transferred to a lightweight robot to perform the ultrasound scan using an impedance control mode. The proposed approach is validated via simulation as well as on phantoms and on animal viscera.

关键词： Ultrasonic imaging Three-dimensional displays Transducers Robot kinematics Trajectory Probes

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Augmented reality-based feedback for technician-in-the-loop c-arm repositioning

arXiv

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arXiv 2018年

作者： Unberath, Mathias Fotouhi, Javad Hajek, Jonas Maier, Andreas Osgood, Greg Taylor, Russell Armand, Mehran Navab, Nassir Computer Aided Medical Procedures Johns Hopkins University Pattern Recognition Laboratory FAU Erlangen-Nuremberg Orthopaedic Trauma Johns Hopkins University Laboratory for Computational Sensing and Robotics Johns Hopkins University Applied Physics Laboratory Johns Hopkins University

Interventional C-arm imaging is crucial to percutaneous orthopedic procedures as it enables the surgeon to monitor the progress of surgery on the anatomy level. Minimally invasive interventions require repeated acquisition of X-ray images from different anatomical views to verify tool placement. Achieving and reproducing these views often comes at the cost of increased surgical time and radiation dose to both patient and staff. This work proposes a marker-free"technician-in-the-loop" Augmented Reality (AR) solution for C-arm repositioning. The X-ray technician operating the C-arm interventionally is equipped with a head-mounted display capable of recording desired C-arm poses in 3D via an integrated infrared sensor. For C-arm repositioning to a particular target view, the recorded C-arm pose is restored as a virtual object and visualized in an AR environment, serving as a perceptual reference for the technician. We conduct experiments in a setting simulating orthopedic trauma surgery. Our proof-of-principle findings indicate that the proposed system can decrease the 2.76 X-ray images required per desired view down to zero, suggesting substantial reductions of radiation dose during C-arm repositioning. The proposed AR solution is a first step towards facilitating communication between the surgeon and the surgical staff, improving the quality of surgical image acquisition, and enabling context-aware guidance for surgery rooms of the future. The concept of technician-in-the-loop design will become relevant to various interventions considering the expected advancements of sensing and wearable computing in the near future. Copyright © 2018, The Authors. All rights reserved.

关键词： Robotic arms

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Incremental scene understanding on dense SLAM

Incremental scene understanding on dense SLAM

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2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

作者： Chi Li Han Xiao Keisuke Tateno Federico Tombari Nassir Navab Gregory D. Hager The Computational Interaction and Robotics Laboratory Johns Hopkins University USA Chair for Computer Aided Medical Procedures (CAMP) TU Munich Munich Germany DISI University of Bologna Italy Johns Hopkins University Baltimore MD US

ISBN: (纸本)9781509037636

We present an architecture for online, incremental scene modeling which combines a SLAM-based scene understanding framework with semantic segmentation and object pose estimation. The core of this approach comprises a probabilistic inference scheme that predicts semantic labels for object hypotheses at each new frame. From these hypotheses, recognized scene structures are incrementally constructed and tracked. Semantic labels are inferred using a multi-domain convolutional architecture which operates on the image time series and which enables efficient propagation of features as well as robust model registration. To evaluate this architecture, we introduce a large-scale RGB-D dataset JHUSEQ-25 as a new benchmark for the sequence-based scene understanding in complex and densely cluttered scenes. This dataset contains 25 RGB-D video sequences with 100,000 labeled frames in total. We validate our method on this dataset and demonstrate improved performance of semantic segmentation and 6-DoF object pose estimation compared with methods based on the single view.

关键词： Semantics Three-dimensional displays Simultaneous localization and mapping GSM Pose estimation Cameras Merging

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