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Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging : MICCAI 2016 International Workshops, MCV and BAMBI, Athens, Greece, October 21, 2016, Revised Selected Papers / edited by Henning Müller, B. Michael Kelm, Tal Arbel, Weidong Cai, M. Jorge Cardoso, Georg Langs, Bjoern Menze, Dimitris Metaxas, Albert Montillo, William M. Wells III, Shaoting Zhang, Albert C.S. Chung, Mark Jenkinson, Annemie Ribbens.
- Format:
- Book
- Series:
- Computer Science (SpringerNature-11645)
- LNCS sublibrary. Image processing, computer vision, pattern recognition, and graphics ; SL 6, 10081
- Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 10081
- Language:
- English
- Subjects (All):
- Computer vision.
- Medical informatics.
- Artificial intelligence.
- Computer science-Mathematics.
- Mathematical statistics.
- Pattern recognition systems.
- Computer Vision.
- Health Informatics.
- Artificial Intelligence.
- Probability and Statistics in Computer Science.
- Mathematical Applications in Computer Science.
- Automated Pattern Recognition.
- Local Subjects:
- Computer Vision.
- Health Informatics.
- Artificial Intelligence.
- Probability and Statistics in Computer Science.
- Mathematical Applications in Computer Science.
- Automated Pattern Recognition.
- Physical Description:
- 1 online resource (XIII, 222 pages) : 75 illustrations
- Edition:
- 1st ed. 2017.
- Contained In:
- Springer Nature eBook
- Place of Publication:
- Cham : Springer International Publishing : Imprint: Springer, 2017.
- System Details:
- text file PDF
- Summary:
- This book constitutes the thoroughly refereed post-workshop proceedings of the International Workshop on Medical Computer Vision, MCV 2016, and of the International Workshop on Bayesian and grAphical Models for Biomedical Imaging, BAMBI 2016, held in Athens, Greece, in October 2016, held in conjunction with the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016. The 13 papers presented in MCV workshop and the 6 papers presented in BAMBI workshop were carefully reviewed and selected from numerous submissions. The goal of the MCV workshop is to explore the use of "big data" algorithms for harvesting, organizing and learning from large-scale medical imaging data sets and for general-purpose automatic understanding of medical images. The BAMBI workshop aims to highlight the potential of using Bayesian or random field graphical models for advancing research in biomedical image analysis.
- Contents:
- Constructing Subject- and Disease-Specific Effect Maps: Application to Neurodegenerative Diseases
- BigBrain: Automated Cortical Parcellation and Comparison with Existing Brain Atlases
- LATEST: Local AdapTivE and Sequential Training for Tissue Segmentation of Isointense Infant Brain MR Images
- Landmark-based Alzheimer's Disease Diagnosis Using Longitudinal Structural MR Images
- Inferring Disease Status by non-Parametric Probabilistic Embedding
- A Lung Graph-Model for Pulmonary Hypertension and Pulmonary Embolism Detection on DECT Images
- Explaining Radiological Emphysema Subtypes with Unsupervised Texture Prototypes: MESA COPD Study
- Automatic Segmentation of Abdominal MRI Using Selective Sampling and Random Walker
- Gaze2Segment: A Pilot Study for Integrating Eye-Tracking Technology into Medical Image Segmentation
- Automatic Detection of Histological Artifacts in Mouse Brain Slice Images
- Lung Nodule Classification by Jointly Using Visual Descriptors and Deep Features
- Representation Learning for Cross-Modality Classification
- Guideline-based Machine Learning for Standard Plane Extraction in 3D Cardiac Ultrasound
- A Statistical Model for Simultaneous Template Estimation, Bias Correction, and Registration of 3D Brain Images
- Bayesian Multiview Manifold Learning Applied to Hippocampus Shape and Clinical Score Data
- Rigid Slice-To-Volume Medical Image Registration through Markov Random Fields
- Sparse Probabilistic Parallel Factor Analysis for the Modeling of PET and Task-fMRI data
- Non-local Graph-based Regularization for Deformable Image Registration
- Unsupervised Framework for Consistent Longitudinal MS Lesion Segmentation. .
- Other Format:
- Printed edition:
- ISBN:
- 978-3-319-61188-4
- 9783319611884
- Access Restriction:
- Restricted for use by site license.
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