检索结果-内蒙古大学图书馆

arXiv 2019年

作者： Cao, Xiaozhi Liao, Congyu Zhang, Zijing Iyer, Siddharth Srinivasan Chen, Zhifeng Lo, Wei-Ching Liu, Huafeng Wang, Kang He, Hongjian Setsompop, Kawin Zhong, Jianhui Bilgic, Berkin Center for Brain Imaging Science and Technology Department of Biomedical Engineering Zhejiang University Zhejiang Hangzhou China Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital CharlestownMA United States Department of Radiology Harvard Medical School CharlestownMA United States State Key Laboratory of Modern Optical Instrumentation College of Optical Science and Engineering Zhejiang University Zhejiang Hangzhou China Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States School of Biomedical Engineering Guangdong Provincial Key Laboratory of Medical Image Processing Southern Medical University Guangzhou China Siemens Medical Solutions BostonMA United States Department of Neurology The First Affiliated Hospital School of Medicine Zhejiang University Hangzhou China Harvard-MIT Department of Health Sciences and Technology CambridgeMA United States Department of Imaging Sciences University of Rochester NY United States

Purpose: To rapidly obtain high isotropic-resolution T2 maps with whole-brain coverage and high geometric fidelity. Methods: A T2 blip-up/down echo planar imaging (EPI) acquisition with generalized Slice-dithered enhanced resolution (T2-BUDA-gSlider) is proposed. A radiofrequency (RF)-encoded multi-slab spin-echo EPI acquisition with multiple echo times (TEs) was developed to obtain high SNR efficiency with reduced repetition time (TR). This was combined with an interleaved 2-shot EPI acquisition using blip-up/down phase encoding. An estimated field map was incorporated into the joint multi-shot EPI reconstruction with a structured low rank constraint to achieve distortion-free and robust reconstruction for each slab without navigation. A Bloch simulated subspace model was integrated into gSlider reconstruction and utilized for T2 quantification. Results: In vivo results demonstrated that the T2 values estimated by the proposed method were consistent with gold standard spin-echo acquisition. Compared to the reference 3D fast spin echo (FSE) images, distortion caused by off-resonance and eddy current effects were effectively mitigated. Conclusion: BUDA-gSlider SE-EPI acquisition and gSlider-subspace joint reconstruction enabled distortion-free whole-brain T2 mapping in 2 min at ~1 mm3 isotropic resolution, which could bring significant benefits to related clinical and neuroscience applications. Copyright © 2019, The Authors. All rights reserved.

关键词： Spin polarization

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3D RoI-aware U-net for accurate and efficient colorectal tumor segmentation

arXiv

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arXiv 2018年

作者： Huang, Yi-Jie Dou, Qi Wang, Zi-Xian Liu, Li-Zhi Jin, Ying Li, Chao-Feng Wang, Lisheng Chen, Hao Xu, Rui-Hua Institute of Image Processing and Pattern Recognition Department of Automation Shanghai Jiao Tong University China Imsight Medical Technology Co. Ltd. China Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong Sun Yat-sen University Cancer Center State Key Laboratory of Oncology in South China Collaborative Innovation Center for Cancer Medicine Guangzhou China

Segmentation of colorectal cancerous regions from 3D Magnetic Resonance (MR) images is a crucial procedure for radiotherapy which conventionally requires accurate delineation of tumour boundaries at an expense of labor, time and reproducibility. While deep learning based methods serve good baselines in 3D image segmentation tasks, small applicable patch size limits effective receptive field and degrades segmentation performance. In addition, Regions of interest (RoIs) localization from large whole volume 3D images serves as a preceding operation that brings about multiple benefits in terms of speed, target completeness, reduction of false positives. Distinct from sliding window or non-joint localization-segmentation based models, we propose a novel multitask framework referred to as 3D RoI-aware U-Net (3D RU-Net), for RoI localization and in-region segmentation where the two tasks share one backbone encoder network. With the region proposals from the encoder, we crop multi-level RoI in-region features from the encoder to form a GPU memory-efficient decoder for detail-preserving segmentation and therefore enlarged applicable volume size and effective receptive field. To effectively train the model, we designed a Dice formulated loss function for the global-to-local multi-task learning procedure. Based on the efficiency gains demonstrated by the proposed method, we went on to ensemble models with different receptive fields to achieve even higher performance costing minor extra computational expensiveness. Extensive experiments were subsequently conducted on 64 cancerous cases with a four-fold cross-validation, and the results showed significant superiority in terms of accuracy and efficiency over conventional state-of-the art frameworks. In conclusion, the proposed method has a huge potential for extension to other 3D object segmentation tasks from medical images due to its inherent generalizability. The code for the proposed method is publicly available. Copyright ©

关键词： Signal encoding

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Total generalized variation for manifold-valued data

arXiv

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arXiv 2017年

作者： Bredies, K. Holler, M. Storath, M. Weinmann, A. Institute of Mathematics and Scientific Computing University of Graz Image Analysis and Learning Group Heidelberg Collaboratory for Image Processing Universität Heidelberg Germany Institute of Computational Biology Helmholtz Zentrum München Department of Mathematics and Natural Sciences Hochschule Darmstadt Germany

In this paper we introduce the notion of second-order total generalized variation (TGV) regularization for manifold-valued data in a discrete setting. We provide an axiomatic approach to formalize reasonable generalizations of TGV to the manifold setting and present two possible concrete instances that fulfill the proposed axioms. We provide well-posedness results and present algorithms for a numerical realization of these generalizations to the manifold setup. Further, we provide experimental results for synthetic and real data to further underpin the proposed generalization numerically and show its potential for applications with manifold-valued data.94A08, 68U10, 90C90, 53B99, 65K10 Copyright © 2017, The Authors. All rights reserved.

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Claire: A distributed-memory solver for constrained large deformation diffeomorphic image registration

arXiv

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arXiv 2018年

作者： Mang, Andreas Gholami, Amir Davatzikos, Christos Biros, George Department of Mathematics University of Houston HoustonTX77204-5008 United States Department of Electrical Engineering and Computer Sciences University of California BerkeleyCA94720-1770 United States Center for Biomedical Image Computing and Analytics Department of Radiology University of Pennsylvania PhiladephiaPA19104-2643 United States Institute for Computational Engineering and Sciences University of Texas at Austin AustinTX78712-1229 United States

68U10, 49J20, 35Q93, 65K10, 65F08, 76D55We introduce CLAIRE, a distributed-memory algorithm and software for solving constrained large deformation diffeomorphic image registration problems in three dimensions. We invert for a stationary velocity field that parameterizes the deformation map. Our solver is based on a globalized, preconditioned, inexact reduced space Gauss-Newton-Krylov scheme. We exploit state-of-the-art techniques in scientific computing to develop an effective solver that scales to thousand of distributed memory nodes on high-end clusters. Our improved, parallel implementation features parameter-, scale-, and grid-continuation schemes to speedup the computations and reduce the likelihood to get trapped in local minima. We also implement an improved preconditioner for the reduced space Hessian to speedup the convergence. We test registration performance on synthetic and real data. We demonstrate registration accuracy on 16 neuroimaging datasets. We compare the performance of our scheme against different flavors of the DEMONS algorithm for diffeomorphic image registration. We study convergence of our preconditioner and our overall algorithm. We report scalability results on state-of-the-art supercomputing platforms. We demonstrate that we can solve registration problems for clinically relevant data sizes in two to four minutes on a standard compute node with 20 cores, attaining excellent data fidelity. With the present work we achieve a speedup of (on average) 5× with a peak performance of up to 17× compared to our former work. Copyright © 2018, The Authors. All rights reserved.

关键词： Neuroimaging

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Personalized pancreatic tumor growth prediction via group learning 1

引用

20th International Conference on Medical image Computing and computer-Assisted Intervention, MICCAI 2017

作者： Zhang, Ling Lu, Le Summers, Ronald M. Kebebew, Electron Yao, Jianhua Imaging Biomarkers and Computer-Aided Diagnosis Laboratory The Clinical Image Processing Service Radiology and Imaging Sciences Department National Institutes of Health Clinical Center BethesdaMD20892 United States Endocrine Oncology Branch National Cancer Institute National Institutes of Health BethesdaMD20892 United States

ISBN: (数字)9783319661858

ISBN: (纸本)9783319661841

Tumor growth prediction, a highly challenging task, has long been viewed as a mathematical modeling problem, where the tumor growth pattern is personalized based on imaging and clinical data of a target patient. Though mathematical models yield promising results, their prediction accuracy may be limited by the absence of population trend data and personalized clinical characteristics. In this paper, we propose a statistical group learning approach to predict the tumor growth pattern that incorporates both the population trend and personalized data. In order to discover high-level features from multimodal imaging data, a deep convolutional neural network approach is developed to model the voxel-wise spatio-temporal tumor progression. The deep features are combined with the time intervals and the clinical factors to feed a process of feature selection. Our predictive model is pretrained on a group data set and personalized on the target patient data to estimate the future spatio-temporal progression of the patient’s tumor. Multimodal imaging data at multiple time points are used in the learning, personalization and inference stages. Our method achieves a Dice coefficient of 86.8% ± 3.6% and RVD of 7.9% ± 5.4% on a pancreatic tumor data set, outperforming the DSC of 84.4% ± 4.0 and RVD 13.9% ± 9.8% obtained by a previous state-of-the-art model-based method. © Springer International Publishing AG (outside the US) 2017.

关键词： Forecasting

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The Liver Tumor Segmentation Benchmark (LiTS)

arXiv

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arXiv 2019年

作者： Bilic, Patrick Christ, Patrick Li, Hongwei Bran Vorontsov, Eugene Ben-Cohen, Avi Kaissis, Georgios Szeskin, Adi Jacobs, Colin Mamani, Gabriel Efrain Humpire Chartrand, Gabriel Lohöfer, Fabian Holch, Julian Walter Sommer, Wieland Hofmann, Felix Hostettler, Alexandre Lev-Cohain, Naama Drozdzal, Michal Amitai, Michal Marianne Vivanti, Refael Sosna, Jacob Ezhov, Ivan Sekuboyina, Anjany Navarro, Fernando Kofler, Florian Paetzold, Johannes C. Shit, Suprosanna Hu, Xiaobin Lipková, Jana Rempfler, Markus Piraud, Marie Kirschke, Jan Wiestler, Benedikt Zhang, Zhiheng Hülsemeyer, Christian Beetz, Marcel Ettlinger, Florian Antonelli, Michela Bae, Woong Bellver, Míriam Bi, Lei Chen, Hao Chlebus, Grzegorz Dam, Erik B. Dou, Qi Fu, Chi-Wing Georgescu, Bogdan Giró-I-Nieto, Xavier Gruen, Felix Han, Xu Heng, Pheng-Ann Hesser, Jürgen Moltz, Jan Hendrik Igel, Christian Isensee, Fabian Jäger, Paul Jia, Fucang Kaluva, Krishna Chaitanya Khened, Mahendra Kim, Ildoo Kim, Jae-Hun Kim, Sungwoong Kohl, Simon Konopczynski, Tomasz Kori, Avinash Krishnamurthi, Ganapathy Li, Fan Li, Hongchao Li, Junbo Li, Xiaomeng Lowengrub, John Ma, Jun Maier-Hein, Klaus Maninis, Kevis-Kokitsi Meine, Hans Merhof, Dorit Pai, Akshay Perslev, Mathias Petersen, Jens Pont-Tuset, Jordi Qi, Jin Qi, Xiaojuan Rippel, Oliver Roth, Karsten Sarasua, Ignacio Schenk, Andrea Shen, Zengming Torres, Jordi Wachinger, Christian Wang, Chunliang Weninger, Leon Wu, Jianrong Xu, Daguang Yang, Xiaoping Yu, Simon Chun-Ho Yuan, Yading Yue, Miao Zhang, Liping Cardoso, Jorge Bakas, Spyridon Braren, Rickmer Heinemann, Volker Pal, Christopher Tang, An Kadoury, Samuel Soler, Luc van Ginneken, Bram Greenspan, Hayit Joskowicz, Leo Menze, Bjoern Department of Informatics Technical University of Munich Germany Department of Quantitative Biomedicine University of Zurich Switzerland Ecole Polytechnique de Montréal Canada Department of Medical Imaging Radboud University Medical Center Nijmegen Netherlands Department of Biomedical Engineering Tel-Aviv University Israel Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany Philips Research China Philips China Innovation Campus Shanghai China School of Biomedical Engineering & Imaging Sciences King0s College London London United Kingdom Institute for AI in Medicine Technical University of Munich Germany Department of Computer Science Guangdong University of Foreign Studies China Institute for diagnostic and interventional radiology Klinikum rechts der Isar Technical University of Munich Germany Institute for Diagnostic and Interventional Neuroradiology Klinikum Rechts der Isar Technical University of Munich Germany Department of Hepatobiliary Surgery The Affiliated Drum Tower Hospital Nanjing University Medical School China Department of Computing Imperial College London London United Kingdom Institute for Tissue Engineering and Regenerative Medicine Helmholtz Zentrum München Neuherberg Germany Brigham and Women's Hospital Harvard Medical School United States School of Computer Science and Engineering The Hebrew University of Jerusalem Israel University of Pennsylvania PA United States Pte. Ltd. Singapore Medical Imaging and Reconstruction Lab Department of Engineering Design Indian Institute of Technology Madras India Sensetime Shanghai China Department of Radiology Perelman School of Medicine University of Pennsylvania United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PA United States Co. Ltd China MontréalQC Canada Department of Radiology Radiation Oncology and Nuclear Medicine University of Montréal

In this work, we report the set-up and results of the Liver Tumor Segmentation Benchmark (LiTS), which was organized in conjunction with the IEEE International Symposium on Biomedical Imaging (ISBI) 2017 and the International Conferences on Medical image Computing and computer-Assisted Intervention (MICCAI) 2017 and 2018. The image dataset is diverse and contains primary and secondary tumors with varied sizes and appearances with various lesion-to-background levels (hyper-/hypo-dense), created in collaboration with seven hospitals and research institutions. Seventy-five submitted liver and liver tumor segmentation algorithms were trained on a set of 131 computed tomography (CT) volumes and were tested on 70 unseen test images acquired from different patients. We found that not a single algorithm performed best for both liver and liver tumors in the three events. The best liver segmentation algorithm achieved a Dice score of 0.963, whereas, for tumor segmentation, the best algorithms achieved Dices scores of 0.674 (ISBI 2017), 0.702 (MICCAI 2017), and 0.739 (MICCAI 2018). Retrospectively, we performed additional analysis on liver tumor detection and revealed that not all top-performing segmentation algorithms worked well for tumor detection. The best liver tumor detection method achieved a lesion-wise recall of 0.458 (ISBI 2017), 0.515 (MICCAI 2017), and 0.554 (MICCAI 2018), indicating the need for further research. LiTS remains an active benchmark and resource for research, e.g., contributing the liver-related segmentation tasks in http://***/. In addition, both data and online evaluation are accessible via ***. © 2019, CC BY-NC-ND.

关键词： Deep learning

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Nonconvex penalties with analytical solutions for one-bit compressive sensing

arXiv

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arXiv 2017年

作者： Huang, Xiaolin Yan, Ming Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University MOE Key Laboratory of System Control and Information Processing Shanghai200240 China Department of Computational Mathematics Science and Engineering Department of Mathematics Michigan State University East LansingMI48824 United States

One-bit measurements widely exist in the real world and can be used to recover sparse signals. This task is known as one-bit compressive sensing (1bit-CS). In this paper, we propose novel algorithms based on both convex and nonconvex sparsity-inducing penalties for robust 1bit-CS. We consider the dual problem, which has only one variable and provides a sufficient condition to verify whether a solution is globally optimal or not. For positive homogeneous penalties, a globally optimal solution can be obtained in two steps: a proximal operator and a normalization step. For other penalties, we solve the dual problem, and it needs to evaluate the proximal operators for many times. Then we provide fast algorithms for finding analytical solutions for three penalties: minimax concave penalty (MCP), 0norm, and sorted 1penalty. Specifically, our algorithm is more than 200 times faster than the existing algorithm for MCP. Its efficiency is comparable to the algorithm for the 1penalty in time, while its performance is much better than 1. Among these penalties, sorted 1is most robust to noise in different settings. Copyright © 2017, The Authors. All rights reserved.

关键词： Compressed sensing

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R3-Net: A deep network for multi-oriented vehicle detection in aerial images and videos

arXiv

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arXiv 2018年

作者： Li, Qingpeng Mou, Lichao Xu, Qizhi Zhang, Yun Zhu, Xiao Xiang State Key Laboratory of Virtual Reality Technology and Systems Beijing Key Laboratory of Digital Media School of Computer Science and Engineering Beihang University Beijing100191 China Remote Sensing Technology Institute German Aerospace Center Wessling82234 Germany Signal Processing in Earth Observation Technical University of Munich Munich80333 Germany Canada Research Chair Laboratory in Advanced Geomatics Image Processing Department of Geodesy and Geomatics Engineering University of New Brunswick FrederictonNBE3B 5A3 Canada

Vehicle detection is a significant and challenging task in aerial remote sensing applications. Most existing methods detect vehicles with regular rectangle boxes and fail to offer the orientation of vehicles. However, the orientation information is crucial for several practical applications, such as the trajectory and motion estimation of vehicles. In this paper, we propose a novel deep network, called rotatable region-based residual network (R3-Net), to detect multi-oriented vehicles in aerial images and videos. More specially, R3-Net is utilized to generate rotatable rectangular target boxes in a half coordinate system. First, we use a rotatable region proposal network (R-RPN) to generate rotatable region of interests (R-RoIs) from feature maps produced by a deep convolutional neural network. Here, a proposed batch averaging rotatable anchor (BAR anchor) strategy is applied to initialize the shape of vehicle candidates. Next, we propose a rotatable detection network (R-DN) for the final classification and regression of the R-RoIs. In RDN, a novel rotatable position sensitive pooling (R-PS pooling) is designed to keep the position and orientation information simultaneously while downsampling the feature maps of RRoIs. In our model, R-RPN and R-DN can be trained jointly. We test our network on two open vehicle detection image datasets, namely DLR 3K Munich Dataset and VEDAI Dataset, demonstrating the high precision and robustness of our method. In addition, further experiments on aerial videos show the good generalization capability of the proposed method and its potential for vehicle tracking in aerial videos. The demo video is available at https://***/xCYD-tYudN0. Copyright © 2018, The Authors. All rights reserved.

关键词： Remote sensing

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Essential guidelines for computational method benchmarking

arXiv

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arXiv 2018年

作者： Weber, Lukas M. Saelens, Wouter Cannoodt, Robrecht Soneson, Charlotte Hapfelmeier, Alexander Gardner, Paul P. Boulesteix, Anne-Laure Saeys, Yvan Robinson, Mark D. Institute of Molecular Life Sciences University of Zurich Zurich Switzerland SIB Swiss Institute of Bioinformatics University of Zurich Zurich Switzerland Data Mining and Modelling for Biomedicine VIB Center for Inflammation Research Ghent Belgium Department of Applied Mathematics Computer Science and Statistics Ghent University Ghent Belgium Institute of Medical Informatics Statistics and Epidemiology Technical University of Munich Munich Germany Department of Biochemistry University of Otago Dunedin New Zealand Institute for Medical Information Processing Biometry and Epidemiology Ludwig-Maximilians-University Munich Germany Friedrich Miescher Institute for Biomedical Research SIB Swiss Institute of Bioinformatics Basel Switzerland

In computational biology and other sciences, researchers are frequently faced with a choice between several computational methods for performing data analyses. Benchmarking studies aim to rigorously compare the performance of different methods using well-characterized benchmark datasets, to determine the strengths of each method or to provide recommendations regarding suitable choices of methods for an analysis. However, benchmarking studies must be carefully designed and implemented to provide accurate, unbiased, and informative results. Here, we summarize key practical guidelines and recommendations for performing high-quality benchmarking analyses, based on our experiences in computational biology. Copyright © 2018, The Authors. All rights reserved.

关键词： Benchmarking

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Multi-materials beam hardening artifacts correction for computed tomography (CT) based on X-ray spectrum estimation

arXiv

引用

arXiv 2018年

作者： Zhao, Wei Li, Dengwang Niu, Kai Qin, Wenjian Peng, Hao Niu, Tianye Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education Nanjing210096 China Shandong Province Key Laboratory of Medical Physics and Image Processing Technology School of Physics and Electronics Shandong Normal University Jinan250014 China Department of Medical Physics University of Wisconsin-Madison MadisonWI53705 United States Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Department of Physics Wuhan University Institute of Translational Medicine

Due to the energy dependent nature of the attenuation coefficient and the polychromaticity of X-ray source, beam hardening effect occurs when X-ray photons penetrate through an object, causing a nonlinear projection data. When a linear reconstruction algorithm, such as filtered backprojection, is applied to reconstruct the projection data, beam hardening artifacts which show as cupping and streaks, are present in the CT image. The aim of this study was to develop a fast and accurate beam hardening correction method which can deal with beam hardening artifacts induced by multi-materials objects. Based on spectrum estimation, the nonlinear attenuation process of the X-ray projection was modeled by reprojecting a template image with the estimated polychromatic spectrum. The template images were obtained by segmenting the uncorrected into different components using a simple segmentation algorithm. By adding the scaled difference of the monochromatic reprojection data and the polychromatic reprojection to the raw projection data, the raw projection data was mapped into the corresponding monochromatic projection data, which was used to reconstruct the beam hardening artifacts corrected images. The algorithm can also be implemented in image-domain which takes the uncorrected image volume as input. In this case, the scaled mapping term was reconstructed to yield a set of artifacts images which can be added directly to the uncorrected images. Numerical simulations, experimental phantom data and animal data which were acquired on a modern diagnostic CT scanner (Discovery CT750 HD, GE Healthcare, WI, USA) and a modern C-Arm CT scanner (Artis Zee, Siemens Healthcare, Forchheim, Germany), respectively, were used to evaluate the proposed method. The results show the proposed method significantly Copyright © 2018, The Authors. All rights reserved.

关键词： computerized tomography

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