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Titlebook: Medical Image Computing and Computer Assisted Intervention – MICCAI 2018; 21st International C Alejandro F. Frangi,Julia A. Schnabel,Gabor

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书目名称Medical Image Computing and Computer Assisted Intervention – MICCAI 2018
副标题21st International C
编辑Alejandro F. Frangi,Julia A. Schnabel,Gabor Fichti
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: Medical Image Computing and Computer Assisted Intervention – MICCAI 2018; 21st International C Alejandro F. Frangi,Julia A. Schnabel,Gabor
描述.The four-volume set LNCS 11070, 11071, 11072, and 11073 constitutes the refereed proceedings of the 21st International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2018, held in Granada, Spain, in September 2018...The 373 revised full papers presented were carefully reviewed and selected from 1068 submissions in a double-blind review process. The papers have been organized in the following topical sections: .Part I: Image Quality and Artefacts; Image Reconstruction Methods; Machine Learning in Medical Imaging; Statistical Analysis for Medical Imaging; Image Registration Methods. . Part II: .Optical and Histology Applications:. Optical Imaging Applications; Histology Applications; Microscopy Applications; Optical Coherence Tomography and Other Optical Imaging Applications. .Cardiac, Chest and Abdominal Applications:. Cardiac Imaging Applications: Colorectal, Kidney and Liver Imaging Applications; Lung Imaging Applications; Breast Imaging Applications; Other Abdominal Applications. .Part III: .Diffusion Tensor Imaging and Functional MRI:. Diffusion Tensor Imaging; Diffusion Weighted Imaging; Functional MRI; Human Connectome. .Neuroimaging and Brai
出版日期Conference proceedings 2018
关键词Artificial intelligence; Classification; Computer vision; Estimation; Image analysis; Image enhancement; I
版次1
doihttps://doi.org/10.1007/978-3-030-00928-1
isbn_softcover978-3-030-00927-4
isbn_ebook978-3-030-00928-1Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightSpringer Nature Switzerland AG 2018
The information of publication is updating

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Conditional Generative Adversarial Networks for Metal Artifact Reduction in CT Images of the Ear segmentations of intra-cochlear anatomical structures, which are obtained with a previously published method, in the real pre-implantation and the artifact-corrected CTs. We show that the proposed method leads to an average surface error of 0.18 mm which is about half of what could be achieved with
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Deep Convolutional Filtering for Spatio-Temporal Denoising and Artifact Removal in Arterial Spin Labn of perfusion, even in challenging datasets, this technique offers an exciting new approach for ASL pipelines, and might be used both for improving individual images and to increase the power of research studies using ASL.
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DeepASL: Kinetic Model Incorporated Loss for Denoising Arterial Spin Labeled MRI via Deep Residual L mapping from noisy perfusion-weighted image and its subtraction (residual) from the clean image. Additionally, we incorporate the CBF estimation model in the loss function during training, which enables the network to produce high quality images while simultaneously enforcing the CBF estimates to b
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Direct Estimation of Pharmacokinetic Parameters from DCE-MRI Using Deep CNN with Forward Physical Mon of PK parameters. Experiments on clinical brain DCE datasets demonstrate the efficacy of our approach in terms of fidelity of PK parameter reconstruction and significantly faster parameter inference compared to a model-based iterative reconstruction method.
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Evaluation of Adjoint Methods in Photoacoustic Tomography with Under-Sampled Sensors BP have better performance on contrast and resolution, respectively. We also show that the integrand of TR includes additional side lobes which degrade axial resolution whereas that of BP conversely has relatively small amplitudes. Moreover, omnidirectional resolution is improved if more sensors ar
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