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deep Learning Assisted Automatic Intra-operative 3d Aortic deformation Reconstruction 23rd

Deep Learning Assisted Automatic Intra-operative 3D Aortic D...

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23rd international conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020

作者： Zhang, Yanhao Falque, Raphael Zhao, Liang Huang, Shoudong Hu, Boni Centre for Autonomous Systems University of Technology Sydney Sydney Australia School of Aeronautics Northwestern Polytechnical University Xian China

ISBN: (纸本)9783030597184

Endovascular interventions rely on 2d X-ray fluoroscopy for 3d catheter manipulation. The dynamic nature of aorta prevents the pre-operative CT/MRI data to be used directly as the live 3d guidance since the vessel deforms during the surgery. This paper provides a framework that reconstructs the live 3d aortic shape by fusing a 3d static pre-operative model and the 2d intra-operative fluoroscopic images. The proposed framework recovers aortic 3d shape automatically and computationally efficient. A deep learning approach is adopted as the front-end for extracting features from fluoroscopic images. A signed distance field based correspondence method is employed for avoiding the repeated feature-vertex matching while maintaining the correspondence accuracy. The warp field of 3d deformation is estimated by solving a non-linear least squares problem based on the embedded deformation graph. detailed phantom experiments are conducted, and the results demonstrate the accuracy of the proposed framework as well as the potential clinical value of the technique. © 2020, Springer Nature Switzerland AG.

关键词： deformation

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Comparative analysis of ros-based centralized methods for conducting collaborative monocular visual slam using a pair of uavs 23rd

Comparative analysis of ros-based centralized methods for co...

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23rd international conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines, CLAWAR 2020

作者： Abbyasov, Bulat Lavrenov, Roman Zakiev, Aufar Tsoy, Tatyana Magid, Evgeni Svinin, Mikhail Martínez-García, Edgar A. Kazan Federal University 35 Kremlyovskaya street Kazan420008 Russia Information Science and Engineering Department College of Information Science and Engineering Ritsumeikan University 1-1-1 Noji-higashi Shiga Kusatsu525-8577 Japan Institute of Engineering and Technology Department of Industrial Engineering and Manufacturing Autonomous University of Ciudad Juarez Manuel Díaz H. No. 518-B Zona Pronaf Condominio Juárez32315 Cd Mexico

ISBN: (纸本)9781916449046

Unmanned Aerial Vehicle (UAV) is a flying robot that acts without a constant human pilot involvement. UAVs are applied in military and civilian areas, in search and rescue operations, 3d mapping, simultaneous localization and mapping (SLAM) and other tasks. SLAM approaches are based on various sensors usage including lidars and cameras. Visual SLAM approaches rely on visual sensing systems and successfully operate within GPS-denied environments. Further, applying several UAVs allows for complex tasks that cannot be handled by a single robot, minimizes exploration time and adds a security level for a case of a single robot failure. This paper presents a comparison of two most applicable vision-based collaborative monocular SLAM methods in Robot operating system, CORB-SLAM and CCM-SLAM, that run on a pair of UAVs. The evaluation is performed on preassembled datasets that correspond to a virtual environment in the Gazebo simulator. The error estimation in virtual experiments demonstrated that CCM-SLAM has a higher global localization accuracy than CORB-SLAM. © CLAWAR Association Ltd.

关键词： Unmanned aerial vehicles (UAV)

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A web Application for Glasses Virtual Try-on in 3d Space 23

A Web Application for Glasses Virtual Try-on in 3D Space

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IEEE 23rd international Symposium on Consumer Technologies (ISCT)

作者： Marelli, davide Bianco, Simone Ciocca, Gianluigi Univ Milano Bicocca DISCo Dept Informat Syst & Commun Viale Sarca 336 I-20126 Milan Italy

ISBN: (纸本)9781728135700

Virtual try-on is a technology that allows people to virtually check the appearance of accessories, makeup, hairstyle, hair color, clothes and potentially more on themselves. The virtual try-on presents many advantages over real try-on, it speeds up the process providing the possibility to test hundreds of products without the need to reach a physical store. In this paper we propose a virtual try-on web application specific for eyeglasses and sunglasses that can be easily used by simply taking a picture of a face and selecting the desired frames. The try-on process is performed on a 3d face reconstructed from the input image allowing the user to see the virtual face and glasses from different viewpoints. The try-on process is fully automated and does not require the user to provide anything else but the picture and selection of the glasses frames to test.

关键词： 3d Face reconstruction Augmented reality Virtual try-on

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deformation Aware Augmented Reality for Craniotomy Using 3d/2d Non-rigid Registration of Cortical Vessels 23rd

Deformation Aware Augmented Reality for Craniotomy Using 3D/...

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23rd international conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020

作者： Haouchine, Nazim Juvekar, Parikshit Wells III, William M. Cotin, Stephane Golby, Alexandra Frisken, Sarah Harvard Medical School Brigham and Womens Hospital BostonMA United States Massachusetts Institute of Technology CambdridgeMA United States Inria Strasbourg France

ISBN: (纸本)9783030597184

Intra-operative brain shift is a well-known phenomenon that describes non-rigid deformation of brain tissues due to gravity and loss of cerebrospinal fluid among other phenomena. This has a negative influence on surgical outcome that is often based on pre-operative planning where the brain shift is not considered. We present a novel brain-shift aware Augmented Reality method to align pre-operative 3d data onto the deformed brain surface viewed through a surgical microscope. We formulate our non-rigid registration as a Shape-from-Template problem. A pre-operative 3d wire-like deformable model is registered onto a single 2d image of the cortical vessels, which is automatically segmented. This 3d/2d registration drives the underlying brain structures, such as tumors, and compensates for the brain shift in sub-cortical regions. We evaluated our approach on simulated and real data composed of 6 patients. It achieved good quantitative and qualitative results making it suitable for neurosurgical guidance. © 2020, Springer Nature Switzerland AG.

关键词： Augmented reality

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Automatic Segmentation, Localization, and Identification of Vertebrae in 3d CT Images Using Cascaded Convolutional Neural Networks 23rd

Automatic Segmentation, Localization, and Identification of ...

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23rd international conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020

作者： Masuzawa, Naoto Kitamura, Yoshiro Nakamura, Keigo Iizuka, Satoshi Simo-Serra, Edgar Imaging Technology Center Fujifilm Corporation TokyoMinato Japan Center for Artificial Intelligence Research University of Tsukuba TsukubaIbaraki Japan Department of Computer Science and Engineering Waseda University TokyoShinjuku Japan

ISBN: (纸本)9783030597245

This paper presents a method for automatic segmentation, localization, and identification of vertebrae in arbitrary 3d CT images. Many previous works do not perform the three tasks simultaneously even though requiring a priori knowledge of which part of the anatomy is visible in the 3d CT images. Our method tackles all these tasks in a single multi-stage framework without any assumptions. In the first stage, we train a 3d Fully Convolutional Networks to find the bounding boxes of the cervical, thoracic, and lumbar vertebrae. In the second stage, we train an iterative 3d Fully Convolutional Networks to segment individual vertebrae in the bounding box. The input to the second networks have an auxiliary channel in addition to the 3d CT images. Given the segmented vertebra regions in the auxiliary channel, the networks output the next vertebra. The proposed method is evaluated in terms of segmentation, localization, and identification accuracy with two public datasets of 15 3d CT images from the MICCAI CSI 2014 workshop challenge and 302 3d CT images with various pathologies introduced in[1]. Our method achieved a mean dice score of 96%, a mean localization error of 8.3mm, and a mean identification rate of 84%. In summary, our method achieved better performance than all existing works in all the three metrics. © 2020, Springer Nature Switzerland AG.

关键词： Iterative methods

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Sea Surface Temperature Prediction Approach Based on 3d CNN and LSTM with Attention Mechanism

Sea Surface Temperature Prediction Approach Based on 3D CNN ...

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international conference on Advanced Communication technology (ICACT)

作者： Baiyou Qiao Zhongqiang Wu Zhong Tang Gang Wu School of Computer Science and Engineering Northeastern University China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University China

ISBN: (数字)9791188428069

ISBN: (纸本)9781665447027

Sea Surface Temperature (SST) is an important physical quantity of the ocean system. The accurate prediction of SST is essential for studying physical ocean phenomena and forecasting the ocean environment information. In this paper, a SST prediction approach based on 3-diminsional Convolutional Neural Network (3d CNN) and Long Short-Term Memory (LSTM) network with attention mechanism is proposed, which considers the spatial correlation and temporal dependency of SST data. Firstly, the machine learning algorithm XGBoost is used to extract the long period time features of each SST data. Then the 3d CNN is used to capture the spatial correlations among SST field data composed of multiple observation points in a selected sea area, followed by the LSTM model to extract the time dependency features of the SST field time series data, and the attention mechanism is added to weight the output of each step of LSTM model to adjust the prediction results and improve the prediction accuracy of the approach. A series of experimental results show that the proposed approach has lower complexity, higher training efficiency and prediction accuracy, which is significantly better than the existing prediction models.

关键词： Sea surface Three-dimensional displays Correlation Computational modeling Predictive models Feature extraction Ocean temperature

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Inferring the 3d Standing Spine Posture from 2d Radiographs 23rd

Inferring the 3D Standing Spine Posture from 2D Radiographs

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23rd international conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020

作者： Bayat, Amirhossein Sekuboyina, Anjany Paetzold, Johannes C. Payer, Christian Stern, darko Urschler, Martin Kirschke, Jan S. Menze, Bjoern H. Department of Informatics Technical University of Munich Munich Germany Department of Neuroradiology Klinikum rechts der Isar Munich Germany Institute of Computer Graphics and Vision Graz University of Technology Graz Austria School of Computer Science University of Auckland Auckland New Zealand

ISBN: (纸本)9783030597245

The treatment of degenerative spinal disorders requires an understanding of the individual spinal anatomy and curvature in 3d. An upright spinal pose (i.e. standing) under natural weight bearing is crucial for such bio-mechanical analysis. 3d volumetric imaging modalities (e.g. CT and MRI) are performed in patients lying down. On the other hand, radiographs are captured in an upright pose, but result in 2d projections. This work aims to integrate the two realms, i.e. it combines the upright spinal curvature from radiographs with the 3d vertebral shape from CT imaging for synthesizing an upright 3d model of spine, loaded naturally. Specifically, we propose a novel neural network architecture working vertebra-wise, termed TransVert, which takes orthogonal 2d radiographs and infers the spines 3d posture. We validate our architecture on digitally reconstructed radiographs, achieving a 3d reconstruction dice of 95.52 %, indicating an almost perfect 2d-to-3d domain translation. deploying our model on clinical radiographs, we successfully synthesise full-3d, upright, patient-specific spine models for the first time. © 2020, Springer Nature Switzerland AG.

关键词： Magnetic resonance imaging

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Applying 3d Vision Sensing to an Industrial Robot for Vehicle Assembly under Noisy Environment 23

Applying 3D Vision Sensing to an Industrial Robot for Vehicl...

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23rd international conference on Mechatronics technology (ICMT)

作者： Cao, Chi-Tho do, Van-Phu Lee, Byung-Ryong Univ Ulsan Sch Mechan & Automat Engn Ulsan South Korea Abeosystem Co LTD Seoul South Korea

ISBN: (纸本)9781728139982

This paper presents a method for automated wheel hub assembly which is important work in the industrial applications. A key requirements for this work are the positioning accuracy of manipulator in working space and the 6d object's pose determined by the 3d vision algorithms. A noncontact method using 3d scanner mounted on the end-effector robot is utilized for providing measurements of object pose in workspace. By controlling the end-effector of robot moving to the working space, the errors of the end-effector are compensated directly. Some experiments to pick up the wheel hub using an industrial robot were implemented to validate the assembly performance of the proposed method.

关键词： 3d vision hand-eye calibration automatic vehicle assembly computed closer point (CCP) iterative closest point (ICP)

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Revisiting Rubiks Cube: Self-supervised Learning with Volume-Wise Transformation for 3d Medical Image Segmentation 23rd

Revisiting Rubiks Cube: Self-supervised Learning with Volume...

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23rd international conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020

作者： Tao, Xing Li, Yuexiang Zhou, Wenhui Ma, Kai Zheng, Yefeng School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Tencent Jarvis Lab Shenzhen China

ISBN: (纸本)9783030597184

deep learning highly relies on the quantity of annotated data. However, the annotations for 3d volumetric medical data require experienced physicians to spend hours or even days for investigation. Self-supervised learning is a potential solution to get rid of the strong requirement of training data by deeply exploiting raw data information. In this paper, we propose a novel self-supervised learning framework for volumetric medical images. Specifically, we propose a context restoration task, i.e., Rubiks cube++, to pre-train 3d neural networks. different from the existing context-restoration-based approaches, we adopt a volume-wise transformation for context permutation, which encourages network to better exploit the inherent 3d anatomical information of organs. Compared to the strategy of training from scratch, fine-tuning from the Rubiks cube++ pre-trained weight can achieve better performance in various tasks such as pancreas segmentation and brain tissue segmentation. The experimental results show that our self-supervised learning method can significantly improve the accuracy of 3d deep learning networks on volumetric medical datasets without the use of extra data. © 2020, Springer Nature Switzerland AG.

关键词： Supervised learning

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deep q-network-driven catheter segmentation in 3d us by hybrid constrained semi-supervised learning and dual-unet 23rd

Deep q-network-driven catheter segmentation in 3d us by hybr...

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23rd international conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020

作者： Yang, Hongxu Shan, Caifeng Kolen, Alexander F. de With, Peter H. N. Eindhoven University of Technology Eindhoven Netherlands Shandong University of Science and Technology Qingdao China Philips Research Eindhoven Netherlands

ISBN: (纸本)9783030597092

Catheter segmentation in 3d ultrasound is important for computer-assisted cardiac intervention. However, a large amount of labeled images are required to train a successful deep convolutional neural network (CNN) to segment the catheter, which is expensive and time-consuming. In this paper, we propose a novel catheter segmentation approach, which requests fewer annotations than the supervised learning method, but nevertheless achieves better performance. Our scheme considers a deep Q learning as the pre-localization step, which avoids voxel-level annotation and which can efficiently localize the target catheter. With the detected catheter, patch-based dual-UNet is applied to segment the catheter in 3d volumetric data. To train the dual-UNet with limited labeled images and leverage information of unlabeled images, we propose a novel semi-supervised scheme, which exploits unlabeled images based on hybrid constraints from predictions. Experiments show the proposed scheme achieves a higher performance than state-of-the-art semi-supervised methods, while it demonstrates that our method is able to learn from large-scale unlabeled images. © Springer Nature Switzerland AG 2020.

关键词： Reinforcement learning

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