LEADER 07104nam 22009135 450 001 9910483568703321 005 20230125005023.0 010 $a3-319-61188-7 024 7 $a10.1007/978-3-319-61188-4 035 $a(CKB)4340000000061594 035 $a(DE-He213)978-3-319-61188-4 035 $a(MiAaPQ)EBC6280991 035 $a(MiAaPQ)EBC5576933 035 $a(Au-PeEL)EBL5576933 035 $a(OCoLC)992975521 035 $a(PPN)202991024 035 $a(EXLCZ)994340000000061594 100 $a20170630d2017 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aMedical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging $eMICCAI 2016 International Workshops, MCV and BAMBI, Athens, Greece, October 21, 2016, Revised Selected Papers /$fedited 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 205 $a1st ed. 2017. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2017. 215 $a1 online resource (XIII, 222 p. 75 illus.) 225 1 $aImage Processing, Computer Vision, Pattern Recognition, and Graphics ;$v10081 300 $aIncludes index. 311 $a3-319-61187-9 327 $aConstructing 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. . 330 $aThis book constitutes the thoroughly refereed post-workshop proceedings of the International Workshop on Medical Computer Vision, MCV 2016, and of the International Workshop on Bayesian and grAphical Models for Biomedical Imaging, BAMBI 2016, held in Athens, Greece, in October 2016, held in conjunction with the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016. The 13 papers presented in MCV workshop and the 6 papers presented in BAMBI workshop were carefully reviewed and selected from numerous submissions. The goal of the MCV workshop is to explore the use of "big data? algorithms for harvesting, organizing and learning from large-scale medical imaging data sets and for general-purpose automatic understanding of medical images. The BAMBI workshop aims to highlight the potential of using Bayesian or random field graphical models for advancing research in biomedical image analysis. 410 0$aImage Processing, Computer Vision, Pattern Recognition, and Graphics ;$v10081 606 $aOptical data processing 606 $aHealth informatics 606 $aArtificial intelligence 606 $aMathematical statistics 606 $aComputer science?Mathematics 606 $aPattern recognition 606 $aImage Processing and Computer Vision$3https://scigraph.springernature.com/ontologies/product-market-codes/I22021 606 $aHealth Informatics$3https://scigraph.springernature.com/ontologies/product-market-codes/I23060 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aProbability and Statistics in Computer Science$3https://scigraph.springernature.com/ontologies/product-market-codes/I17036 606 $aMath Applications in Computer Science$3https://scigraph.springernature.com/ontologies/product-market-codes/I17044 606 $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X 615 0$aOptical data processing. 615 0$aHealth informatics. 615 0$aArtificial intelligence. 615 0$aMathematical statistics. 615 0$aComputer science?Mathematics. 615 0$aPattern recognition. 615 14$aImage Processing and Computer Vision. 615 24$aHealth Informatics. 615 24$aArtificial Intelligence. 615 24$aProbability and Statistics in Computer Science. 615 24$aMath Applications in Computer Science. 615 24$aPattern Recognition. 676 $a610.28 702 $aMüller$b Henning$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aKelm$b B. Michael$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aArbel$b Tal$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aCai$b Weidong$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aCardoso$b M. Jorge$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aLangs$b Georg$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aMenze$b Bjoern$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aMetaxas$b Dimitris$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aMontillo$b Albert$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aWells$b William M.$cIII$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aZhang$b Shaoting$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aChung$b Albert C.S$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aJenkinson$b Mark$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aRibbens$b Annemie$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910483568703321 996 $aMedical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging$92830708 997 $aUNINA