LEADER 06745nam 22008655 450 001 9910483732403321 005 20200630221438.0 010 $a3-319-28194-1 024 7 $a10.1007/978-3-319-28194-0 035 $a(CKB)4340000000001268 035 $a(SSID)ssj0001616816 035 $a(PQKBManifestationID)16348133 035 $a(PQKBTitleCode)TC0001616816 035 $a(PQKBWorkID)14922469 035 $a(PQKB)10888506 035 $a(DE-He213)978-3-319-28194-0 035 $a(MiAaPQ)EBC6296785 035 $a(MiAaPQ)EBC5577013 035 $a(Au-PeEL)EBL5577013 035 $a(OCoLC)1066198907 035 $a(PPN)191705446 035 $a(EXLCZ)994340000000001268 100 $a20160107d2015 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aPatch-Based Techniques in Medical Imaging$b[electronic resource] $eFirst International Workshop, Patch-MI 2015, Held in Conjunction with MICCAI 2015, Munich, Germany, October 9, 2015, Revised Selected Papers /$fedited by Guorong Wu, Pierrick Coupé, Yiqiang Zhan, Brent Munsell, Daniel Rueckert 205 $a1st ed. 2015. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2015. 215 $a1 online resource (IX, 216 p. 81 illus. in color.) 225 1 $aImage Processing, Computer Vision, Pattern Recognition, and Graphics ;$v9467 300 $aIncludes index. 311 $a3-319-28193-3 327 $aA Multi-level Canonical Correlation Analysis Scheme for Standard-dose PET Image Estimation -- Image Super-Resolution by Supervised Adaption of Patchwise Self-Similarity from High-Resolution Image -- Automatic Hippocampus Labeling Using the Hierarchy of Sub-Region Random Forests -- Isointense Infant Brain Segmentation by Stacked Kernel Canonical Correlation Analysis -- Improving Accuracy of Automatic Hippocampus Segmentation in Routine MRI by Features Learned from Ultra-high Field MRI -- Dual-Layer l1-Graph Embedding for Semi-Supervised Image Labeling -- Automatic Liver Tumor Segmentation in Follow-up CT Studies Using Convolutional Neural Network -- Block-based Statistics for Robust Non-Parametric Morphometry -- Automatic Collimation Detection in Digital Radiographs with the Directed Hough Transform and Learning-based Edge Detection -- Efficient Lung Cancer Cell Detection with Deep Convolutional Neural Network -- An Effective Approach for Robust Lung Cancer Cell Detection -- Laplacian Shape Editing with Local Patch Based Force Field for Interactive Segmentation -- Hippocampus Segmentation through Distance Field Fusion -- Learning a Spatiotemporal Dictionary for Magnetic Resonance Fingerprinting with Compress Sensing -- Fast Regions-of-Interest Detection in Whole Slide Histopathology Images -- Reliability Guided Forward and Backward Patch-based Method for Multi-atlas Segmentation -- Correlating Tumour Histology and ex vivo MRI Using Dense Modality-Independent Patch-Based Descriptor -- Multi-Atlas Segmentation using Patch-Based Joint Label Fusion with Non-Negative Least Squares Regression -- A Spatially Constrained Deep Learning Framework for Detection of Epithelial Tumor Nuclei in Cancer Histology Images -- 3D MRI Denoising using Rough Set Theory and Kernel Embedding Method -- A Novel Cell Orientation Congruence Descriptor for Superpixel based Epithelium Segmentation in Endometrial Histology Images -- Patch-based Segmentation from MP2RAGE Images: Comparison to Conventional Techniques -- Multi-Atlas and Multi-Modal Hippocampus Segmentation for Infant MR Brain Images by Propagating Anatomical Labels on Hypergraph -- Prediction of Infant MRI Appearance and Anatomical Structure Evolution using Sparse Patch-based Metamorphosis Learning Framework -- Efficient Multi-Scale Patch-based Segmentation. 330 $aThis book constitutes the thoroughly refereed post-workshop proceedings of the First International Workshop on Patch-based Techniques in Medical Images, Patch-MI 2015, which was held in conjunction with MICCAI 2015, in Munich, Germany, in October 2015. The 25 full papers presented in this volume were carefully reviewed and selected from 35 submissions. The topics covered are such as image segmentation of anatomical structures or lesions; image enhancement; computer-aided prognostic and diagnostic; multi-modality fusion; mono and multi modal image synthesis; image retrieval; dynamic, functional physiologic and anatomic imaging; super-pixel/voxel in medical image analysis; sparse dictionary learning and sparse coding; analysis of 2D, 2D+t, 3D, 3D+t, 4D, and 4D+t data. 410 0$aImage Processing, Computer Vision, Pattern Recognition, and Graphics ;$v9467 606 $aOptical data processing 606 $aPattern recognition 606 $aComputer graphics 606 $aArtificial intelligence 606 $aComputer simulation 606 $aAlgorithms 606 $aImage Processing and Computer Vision$3https://scigraph.springernature.com/ontologies/product-market-codes/I22021 606 $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X 606 $aComputer Graphics$3https://scigraph.springernature.com/ontologies/product-market-codes/I22013 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aSimulation and Modeling$3https://scigraph.springernature.com/ontologies/product-market-codes/I19000 606 $aAlgorithm Analysis and Problem Complexity$3https://scigraph.springernature.com/ontologies/product-market-codes/I16021 615 0$aOptical data processing. 615 0$aPattern recognition. 615 0$aComputer graphics. 615 0$aArtificial intelligence. 615 0$aComputer simulation. 615 0$aAlgorithms. 615 14$aImage Processing and Computer Vision. 615 24$aPattern Recognition. 615 24$aComputer Graphics. 615 24$aArtificial Intelligence. 615 24$aSimulation and Modeling. 615 24$aAlgorithm Analysis and Problem Complexity. 676 $a616.07540285 702 $aWu$b Guorong$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aCoupé$b Pierrick$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aZhan$b Yiqiang$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aMunsell$b Brent$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aRueckert$b Daniel$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910483732403321 996 $aPatch-Based Techniques in Medical Imaging$91918861 997 $aUNINA