Medical image computing and computer assisted intervention -- MICCAI 2019 : 22nd International Conference, Shenzhen, China, October 13-17, 2019, Proceedings. Part II /

Medical image computing and computer assisted intervention -- MICCAI 2019 : 22nd International Conference, Shenzhen, China, October 13-17, 2019, Proceedings. Part II /

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Bibliographic Details
Meeting name:International Conference on Medical Image Computing and Computer-Assisted Intervention (22nd : 2019 : Shenzhen Shi, China)
Imprint:Cham, Switzerland : Springer, 2019.
Description:1 online resource (xxxviii, 874 pages)
Language:English
Series:Lecture notes in computer science ; 11765
LNCS sublibrary. SL 6, Image processing, computer vision, pattern recognition, and graphics
Lecture notes in computer science ; 11765.
LNCS sublibrary. SL 6, Image processing, computer vision, pattern recognition, and graphics.
Subject:
Diagnostic imaging -- Data processing -- Congresses.
Computer-assisted surgery -- Congresses.
Computer-assisted surgery.
Diagnostic imaging -- Data processing.
Electronic books.
Conference papers and proceedings.
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/11981516
Hidden Bibliographic Details
Varying Form of Title:MICCAI 2019
Other authors / contributors:Shen, Dinggang, editor.
Liu, Tianming, Dr., editor.
Peters, Terry M., 1948 January 5- editor.
Staib, Lawrence, editor.
Essert, Caroline, editor.
Zhou, Xiangyun Sean, editor.
Yap, Pew-Thian, editor.
Khan, Ali, editor.
ISBN:9783030322458
3030322459
9783030322441
Digital file characteristics:text file PDF
Notes:International conference proceedings.
Includes author index.
Online resource; title from PDF title page (SpringerLink, viewed October 15, 2019).
Summary:The 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.
Other form:Printed edition: 9783030322441
Printed edition: 9783030322465
Standard no.:10.1007/978-3-030-32245-8
Table of Contents:
  • Arameters 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.

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