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

✍ Scribed by Arbel, Tal; Cai, Weidong; Cardoso, M. Jorge; Chung, Albert C.S; Jenkinson, Mark; Kelm, B. Michael; Langs, Georg; Menze, Bjoern; Metaxas, Dimitris; Montillo, Albert; Müller, Henning; Ribbens, Annemie; Wells III, William M.; Zhang, Shaoting

• Publisher: Springer International Publishing
• Year: 2017
• Tongue: English
• Leaves: 227
• Series: Lecture Notes in Computer Science 10081
• Category: Library

✦ Synopsis

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. Read more...

Abstract: 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

✦ Table of Contents

Front Matter ....Pages I-XIII
Front Matter ....Pages 1-1
Constructing Subject- and Disease-Specific Effect Maps: Application to Neurodegenerative Diseases (Ender Konukoglu, Ben Glocker)....Pages 3-13
BigBrain: Automated Cortical Parcellation and Comparison with Existing Brain Atlases (Marc Fournier, Lindsay B. Lewis, Alan C. Evans)....Pages 14-25
LATEST: Local AdapTivE and Sequential Training for Tissue Segmentation of Isointense Infant Brain MR Images (Li Wang, Yaozong Gao, Gang Li, Feng Shi, Weili Lin, Dinggang Shen)....Pages 26-34
Landmark-Based Alzheimer’s Disease Diagnosis Using Longitudinal Structural MR Images (Jun Zhang, Mingxia Liu, Le An, Yaozong Gao, Dinggang Shen)....Pages 35-45
Front Matter ....Pages 47-47
Inferring Disease Status by Non-parametric Probabilistic Embedding (Nematollah Kayhan Batmanghelich, Ardavan Saeedi, Raul San Jose Estepar, Michael Cho, William M. Wells III)....Pages 49-57
A Lung Graph–Model for Pulmonary Hypertension and Pulmonary Embolism Detection on DECT Images (Yashin Dicente Cid, Henning Müller, Alexandra Platon, Jean–Paul Janssens, Frédéric Lador, Pierre–Alexandre Poletti et al.)....Pages 58-68
Explaining Radiological Emphysema Subtypes with Unsupervised Texture Prototypes: MESA COPD Study (Jie Yang, Elsa D. Angelini, Benjamin M. Smith, John H. M. Austin, Eric A. Hoffman, David A. Bluemke et al.)....Pages 69-80
Front Matter ....Pages 81-81
Automatic Segmentation of Abdominal MRI Using Selective Sampling and Random Walker (Janine Thoma, Firat Ozdemir, Orcun Goksel)....Pages 83-93
Gaze2Segment: A Pilot Study for Integrating Eye-Tracking Technology into Medical Image Segmentation (Naji Khosravan, Haydar Celik, Baris Turkbey, Ruida Cheng, Evan McCreedy, Matthew McAuliffe et al.)....Pages 94-104
Automatic Detection of Histological Artifacts in Mouse Brain Slice Images (Nitin Agarwal, Xiangmin Xu, M. Gopi)....Pages 105-115
Lung Nodule Classification by Jointly Using Visual Descriptors and Deep Features (Yutong Xie, Jianpeng Zhang, Sidong Liu, Weidong Cai, Yong Xia)....Pages 116-125
Representation Learning for Cross-Modality Classification (Gijs van Tulder, Marleen de Bruijne)....Pages 126-136
Guideline-Based Machine Learning for Standard Plane Extraction in 3D Cardiac Ultrasound (Peifei Zhu, Zisheng Li)....Pages 137-147
Front Matter ....Pages 149-149
A Statistical Model for Simultaneous Template Estimation, Bias Correction, and Registration of 3D Brain Images (Akshay Pai, Stefan Sommer, Lars Lau Raket, Line Kühnel, Sune Darkner, Lauge Sørensen et al.)....Pages 151-159
Bayesian Multiview Manifold Learning Applied to Hippocampus Shape and Clinical Score Data (Giorgos Sfikas, Christophoros Nikou)....Pages 160-171
Rigid Slice-To-Volume Medical Image Registration Through Markov Random Fields (Roque Porchetto, Franco Stramana, Nikos Paragios, Enzo Ferrante)....Pages 172-185
Sparse Probabilistic Parallel Factor Analysis for the Modeling of PET and Task-fMRI Data (Vincent Beliveau, Georgios Papoutsakis, Jesper Løve Hinrich, Morten Mørup)....Pages 186-198
Non-local Graph-Based Regularization for Deformable Image Registration (Bartłomiej W. Papież, Adam Szmul, Vicente Grau, J. Michael Brady, Julia A. Schnabel)....Pages 199-207
Unsupervised Framework for Consistent Longitudinal MS Lesion Segmentation (Saurabh Jain, Annemie Ribbens, Diana M. Sima, Sabine Van Huffel, Frederik Maes, Dirk Smeets)....Pages 208-219
Back Matter ....Pages 221-222

✦ Subjects

Computer science;Informatique;Health informatics;Mathematical statistics;Computer science -- Mathematics;Informatique -- Mathématiques;Artificial intelligence;Intelligence artificielle;Image processing;Traitement d’images;Pattern recognition;Medical informatics;Pattern perception