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000915836 020__ $$a9783030322458$$q(electronic book)
000915836 020__ $$a3030322459$$q(electronic book)
000915836 020__ $$z9783030322441
000915836 0247_ $$a10.1007/978-3-030-32245-8$$2doi
000915836 035__ $$aSP(OCoLC)on1123174803
000915836 035__ $$aSP(OCoLC)1123174803
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000915836 049__ $$aISEA
000915836 050_4 $$aRC78.7.D53
000915836 08204 $$a616.07/57$$223
000915836 1112_ $$aInternational Conference on Medical Image Computing and Computer-Assisted Intervention$$n(22nd :$$d2019 :$$cShenzhen Shi, China)
000915836 24510 $$aMedical image computing and computer assisted intervention -- MICCAI 2019 :$$b22nd International Conference, Shenzhen, China, October 13-17, 2019, Proceedings.$$nPart II /$$cDinggang Shen, Tianming Liu, Terry M. Peters, Lawrence H. Staib, Caroline Essert, Sean Zhou, Pew-Thian Yap, Ali Khan (eds.).
000915836 2463_ $$aMICCAI 2019
000915836 264_1 $$aCham, Switzerland :$$bSpringer,$$c2019.
000915836 300__ $$a1 online resource (xxxviii, 874 pages).
000915836 336__ $$atext$$btxt$$2rdacontent
000915836 337__ $$acomputer$$bc$$2rdamedia
000915836 338__ $$aonline resource$$bcr$$2rdacarrier
000915836 4901_ $$aLecture notes in computer science ;$$v11765
000915836 4901_ $$aLNCS sublibrary. SL 6, Image processing, computer vision, pattern recognition, and graphics
000915836 500__ $$aInternational conference proceedings.
000915836 500__ $$aIncludes author index.
000915836 5050_ $$aArameters estimation for brain tumor growth modeling -- Learning-Guided Infinite Network Atlas Selection for Predicting Longitudinal Brain Network Evolution from a Single Observation -- Deep Probabilistic Modeling of Glioma Growth -- Surface-Volume Consistent Construction of Longitudinal Atlases for the Early Developing Brains -- Variational Autoencoder for Regression: Application to Brain Aging Analysis -- Early Development of Infant Brain Complex Network -- Revealing Developmental Regionalization of Infant Cerebral Cortex Based on Multiple Cortical Properties -- Continually Modeling Alzheimer's Disease Progression via Deep Multi-Order Preserving Weight Consolidation -- Disease Knowledge Transfer across Neurodegenerative Diseases i-supervised 3D Left Atrium Segmentation -- MSU-Net: Multiscale Statistical U-Net for Real-time 3D Cardiac MRI Video Segmentation -- The Domain Shift Problem of Medical Image Segmentation and Vendor-Adaptation by Unet-GAN -- Cardiac MRI Segmentation with Strong Anatomical Guarantees -- Decompose-and-Integrate Learning for Multi-class Segmentation in Medical Images -- Missing Slice Imputation in Population CMR Imaging via Conditional Generative Adversarial Nets -- Unsupervised Standard Plane Synthesis in Population Cine MRI via Cycle-Consistent Adversarial Networks -- Data Efficient Unsupervised Domain Adaptation for Cross-Modality Image Segmentation -- Recurrent Aggregation Learning for Multi-View Echocardiographic Sequences Segmentation -- Echocardiography View Classification Using Quality Transfer Star Generative Adversarial Networks -- Dual-view Joint Estimation of Left Ventricular Ejection Fraction with Uncertainty Modelling in Echocardiograms -- Frame Rate Up-Conversion in Echocardiography Using a Conditioned Variational Autoencoder and Generative Adversarial Model -- Annotation-Free Cardiac Vessel Segmentation via Knowledge Transfer from Retinal Images -- DeepAAA: clinically applicable and generalizable detection of abdominal aortic aneurysm using deep learning -- Texture-based classification of significant stenosis in CCTA multi-view images of coronary arteries -- Fourier Spectral Dynamic Data Assimilation: Interlacing CFD with 4D flow MRI -- Quality Control-Driven Image Segmentation Towards Reliable Automatic Image Analysis in Large-Scale Cardiovascular Magnetic Resonance Aortic Cine Imaging -- HFA-Net: 3D Cardiovascular Image Segmentation with Asymmetrical Pooling and Content-Aware Fusion -- Spectral CT based training dataset generation and augmentation for conventional CT vascular segmentation -- Context-Aware Inductive Bias Learning for Vessel Border Detection in Multi-modal Intracoronary Imaging -- Growth, Development, Atrophy and Progression -- Neural p.
000915836 506__ $$aAccess limited to authorized users.
000915836 520__ $$aThe six-volume set LNCS 11764, 11765, 11766, 11767, 11768, and 11769 constitutes the refereed proceedings of the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, held in Shenzhen, China, in October 2019. The 539 revised full papers presented were carefully reviewed and selected from 1730 submissions in a double-blind review process. The papers are organized in the following topical sections: Part I: optical imaging; endoscopy; microscopy. Part II: image segmentation; image registration; cardiovascular imaging; growth, development, atrophy and progression. Part III: neuroimage reconstruction and synthesis; neuroimage segmentation; diffusion weighted magnetic resonance imaging; functional neuroimaging (fMRI); miscellaneous neuroimaging. Part IV: shape; prediction; detection and localization; machine learning; computer-aided diagnosis; image reconstruction and synthesis. Part V: computer assisted interventions; MIC meets CAI. Part VI: computed tomography; X-ray imaging.
000915836 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed October 15, 2019).
000915836 650_0 $$aDiagnostic imaging$$xData processing$$vCongresses.
000915836 650_0 $$aComputer-assisted surgery$$vCongresses.
000915836 7001_ $$aShen, Dinggang,$$eeditor.
000915836 7001_ $$aLiu, Tianming,$$cDr.,$$eeditor.
000915836 7001_ $$aPeters, Terry M.,$$d1948 January 5-$$eeditor.
000915836 7001_ $$aStaib, Lawrence,$$eeditor.
000915836 7001_ $$aEssert, Caroline,$$eeditor.
000915836 7001_ $$aZhou, Xiangyun Sean,$$eeditor.
000915836 7001_ $$aYap, Pew-Thian,$$eeditor.
000915836 7001_ $$aKhan, Ali,$$eeditor.
000915836 830_0 $$aLecture notes in computer science ;$$v11765.
000915836 830_0 $$aLNCS sublibrary.$$nSL 6,$$pImage processing, computer vision, pattern recognition, and graphics.
000915836 852__ $$bebk
000915836 85640 $$3SpringerLink$$uhttps://univsouthin.idm.oclc.org/login?url=http://link.springer.com/10.1007/978-3-030-32245-8$$zOnline Access$$91397441.1
000915836 909CO $$ooai:library.usi.edu:915836$$pGLOBAL_SET
000915836 980__ $$aEBOOK
000915836 980__ $$aBIB
000915836 982__ $$aEbook
000915836 983__ $$aOnline
000915836 994__ $$a92$$bISE