LEADER 14103nam 22009135 450 001 9910484376703321 005 20200705080748.0 010 $a3-319-24574-0 024 7 $a10.1007/978-3-319-24574-4 035 $a(CKB)3150000000021315 035 $a(SSID)ssj0001585307 035 $a(PQKBManifestationID)16264062 035 $a(PQKBTitleCode)TC0001585307 035 $a(PQKBWorkID)14865552 035 $a(PQKB)11599805 035 $a(DE-He213)978-3-319-24574-4 035 $a(MiAaPQ)EBC6287225 035 $a(MiAaPQ)EBC5590589 035 $a(Au-PeEL)EBL5590589 035 $a(OCoLC)1066177247 035 $a(PPN)190528710 035 $a(EXLCZ)993150000000021315 100 $a20150924d2015 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt 182 $cc 183 $acr 200 10$aMedical Image Computing and Computer-Assisted Intervention ? MICCAI 2015 $e18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III /$fedited by Nassir Navab, Joachim Hornegger, William M. Wells, Alejandro Frangi 205 $a1st ed. 2015. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2015. 215 $a1 online resource (XXVII, 780 p. 323 illus., 123 illus. in color.) 225 1 $aImage Processing, Computer Vision, Pattern Recognition, and Graphics ;$v9351 300 $aBibliographic Level Mode of Issuance: Monograph 311 $a3-319-24573-2 327 $aIntro -- Preface -- Organization -- Contents - Part III -- Quantitative Image Analysis I: Segmentation and Measurement -- Deep Convolutional Encoder Networks for Multiple Sclerosis Lesion Segmentation -- 1 Introduction -- 2 Methods -- 3 Experiments and Results -- 4 Conclusions -- References -- Unsupervised Myocardial Segmentation for Cardiac MRI -- 1 Introduction -- 2 Related Work -- 3 Method -- 4 Results -- 5 Discussions and Conclusion -- References -- Multimodal Cortical Parcellation Based on Anatomical and Functional Brain Connectivity -- 1 Introduction -- 2 Materials -- 2.1 RS-fMRI Data -- 2.2 dMRI Data -- 3 Methods -- 3.1 Estimating Brain Connectivity -- 3.2 Adaptively Weighted Multimodal Connectivity Model -- 3.3 Affinity Matrix Estimation -- 4 Results and Discussion -- 4.1 Quantitative Results -- 4.2 Qualitative Results -- 5 Conclusions -- References -- Slic-Seg: Slice-by-Slice Segmentation Propagation of the Placenta in Fetal MRI Using One-Plane Scribbles and Online Learning -- 1 Introduction -- 2 Methods -- 3 Experiments and Results -- 4 Conclusion -- References -- GPSSI: Gaussian Process for Sampling Segmentations of Images -- 1 Introduction -- 2 Existing Generative Models of Segmentations -- 3 GPSSI -- 3.1 Definition -- 3.2 Efficient Sampling -- 4 Results -- 4.1 Parameter Setting -- 4.2 Segmentation Sampling -- 5 Tumor Delineation Uncertainty in Radiotherapy -- 6 Conclusion -- References -- Multi-Level Parcellation of the Cerebral Cortex Using Resting-State fMRI -- 1 Introduction -- 2 Methodology -- 2.1 Data Acquisition and Preprocessing -- 2.2 Initial Parcellation via Supervertex Clustering -- 2.3 Single-Level Parcellation via Hierarchical Clustering -- 2.4 Groupwise Parcellation via Spectral Clustering -- 3 Results -- 4 Conclusions -- References -- Interactive Multi-organ Segmentation Based on Multiple Template Deformation. 327 $a1 Medical Motivation and Overview -- 2 Methodology -- 2.1 Multiple Implicit Template Deformation with User Constraints -- 2.2 Numerical Optimization -- 2.3 Enhancing the Framework for Local Contours Editing -- 2.4 Flexibility of the Framework -- 3 A Study for the Evaluation of the User Interactions -- 4 Conclusion -- References -- Segmentation of Infant Hippocampus Using Common Feature Representations Learned for Multimodal Longitudinal Data -- 1 Introduction -- 2 Method -- 2.1 Hierarchical Learn ning of Common Feature Representations -- 2.2 Patch-Based Label Fusion for Hippocampus Segmentation -- 3 Experimental Results -- 4 Conclusion -- References -- Measuring Cortical Neurite-Dispersion and Perfusion in Preterm-Born Adolescents Using Multi-modal MRI -- 1 Introduction -- 2 Methods -- 3 Results -- 3.1 Comparison of DWI with and without Additional T2 Imaging -- 3.2 Comparison of Quantitative Neuroimaging Parameters -- 3.3 Correlation of Diffusion MRI and Cerebral Blood Flow -- 4 Discussion -- References -- Interactive Whole-Heart Segmentation in Congenital Heart Disease -- 1 Introduction -- 2 Patch-Based Interactive Segmentation -- 3 Empirical Study: Active Learning for Reference Selection -- 4 Results -- 5 Conclusions -- References -- Automatic 3D US Brain Ventricle Segmentation in Pre-Term Neonates Using Multi-phase Geodesic Level-Sets with Shape Prior -- 1 Introduction -- 2 Method -- 3 Experiments and Results -- 4 Discussion and Conclusion -- References -- Multiple Surface Segmentation Using Truncated Convex Priors -- 1 Introduction -- 2 Method -- 3 Experimental Methods -- 4 Results -- 5 Discussion and Conclusion -- References -- Statistical Power in Image Segmentation: Relating Sample Size to Reference Standard Quality -- 1 Introduction -- 2 Derivation of the Sample Size Formula -- 2.1 Sample Size for Segmentation Accuracy. 327 $a2.2 Sample Size in Terms of the High Quality Reference Standard -- 3 Simulations -- 4 Results -- 5 Case Study -- 6 Discussion -- References -- Joint Learning of Image Regressor and Classifier for Deformable Segmentation of CT Pelvic Organs -- 1 Introduction -- 2 Method -- 2.1 Joint Learning of Image Regressor and Classifier -- 2.2 Deformable Segmentation with Regressor and Classifier -- 3 Experimental Results -- 4 Conclusion -- References -- Corpus Callosum Segmentation in MS Studies Using Normal Atlases and Optimal Hybridization of Extrinsic and Intrinsic Image Cues -- 1 Introduction -- 2 Methods -- 3 Evaluation Results -- 4 Conclusions -- References -- Brain Tissue Segmentation Based on Diffusion MRI Using l 0 Sparse-Group Representation Classification -- 1 Introduction -- 2 Approach -- 3 Experiments -- 3.1 Data -- 3.2 Diffusion Parameters -- 3.3 Comparison Methods -- 3.4 Results -- 4 Conclusion -- References -- A Latent Source Model for Patch-Based Image Segmentation -- 1 Introduction -- 2 Pointwise Segmentation and a Theoretical Guarantee -- 3 Multipoint Segmentation -- 4 Conclusions -- References -- Multi-organ Segmentation Using Shape Model Guided Local Phase Analysis -- 1 Introduction -- 2 Method -- 2.1 Quadrature Filters and Model Guided Local Phase Analysis -- 2.2 Integrating Region-Based and Edge-Based Energy in the Level-Set Method -- 2.3 Hierarchical Segmentation Pipeline and Multi-scale Phase Analysis -- 3 Experiments and Results -- 4 Discussion and Conclusion -- References -- Filling Large Discontinuities in 3D Vascular Networks Using Skeleton- and Intensity-Based Information -- 1 Introduction -- 2 Methodology -- 2.1 Method Overview -- 2.2 Gap Filling -- 2.3 Generating the Second-Order Tensor Field T -- 2.4 Deriving a Saliency Map S and Preferential Directions D from T -- 2.5 Generating the Enhancement Map E -- 3 Results. 327 $a3.1 Synthetic Data -- 3.2 3D Images of Tumour Vasculature -- 4 Discussion -- References -- A Continuous Flow-Maximisation Approach to Connectivity-Driven Cortical Parcellation -- 1 Introduction -- 2 Methodology -- 2.1 Iterative Markov Random Field Formulation -- 2.2 Continuous Max-Flow Optimisation -- 3 Results -- 4 Discussion -- References -- A 3D Fractal-Based Approach towards Understanding Changes in the Infarcted Heart Microvasculature -- 1 Introduction -- 2 Methods -- 2.1 Data Acquisition and Pre-processing -- 2.2 Segmentation -- 2.3 Fractal-Based Methods -- 3 Results -- 4 Conclusions -- References -- Segmenting the Uterus in Monocular Laparoscopic Images without Manual Input -- 1 Introduction and Background -- 2 Methodology -- 3 Experimental Results -- 4 Conclusion -- References -- Progressive Label Fusion Framework for Multi-atlas Segmentation by Dictionary Evolution -- 1 Introduction -- 2 Proposed Method -- 2.1 Dictionary Construction -- 2.2 Multi-layer Label Fusion -- 3 Experiments -- 3.1 Dataset and Parameters -- 3.2 Hippocampus Segmentation Results -- 4 Conclusion -- References -- Multi-atlas Based Segmentation Editing with Interaction-Guided Constraints -- 1 Introduction -- 2 Multi-atlas Based Editing Method -- 2.1 Extraction of Local Interaction Combinations -- 2.2 Selection of Training Labels with respect to User Interactions -- 2.3 Label Fusion Based on User Interactions -- 3 Experimental Result -- 4 Conclusion -- References -- Quantitative Image Analysis II: Microscopy, Fluorescence and Histological Imagery -- Improving Convenience and Reliability of 5-ALA-Induced Fluorescent Imaging for Brain Tumor Surgery -- 1 Introduction -- 2 Concept of the Image Acquisition System -- 3 Imaging Formula -- 4 Experiment -- 4.1 Prototype System Building -- 4.2 Real Time Image Processing and Display -- 4.3 Quantitative Imaging Technique. 327 $a5 Discussion and Conclusions -- References -- Analysis of High-throughput Microscopy Videos: Catching Up with Cell Dynamics -- 1 Introduction -- 2 Methods -- 2.1 Problem Formulation -- 2.2 Time Series Analysis -- 2.3 Dynamic Shape Model -- 2.4 MAP Segmentation and Association -- 3 Experimental Results -- 4 Summary and Conclusions -- References -- Neutrophils Identification by Deep Learning and Voronoi Diagram of Clusters -- 1 Introduction -- 2 Modeling Individual Cell Appearances by CNN -- 3 Modeling Cell Context by VDC -- 4 Experiments and Evaluation -- 5 Conclusions -- References -- U-Net: Convolutional Networks for Biomedical Image Segmentation -- 1 Introduction -- 2 Network Architecture -- 3 Training -- 3.1 Data Augmentation -- 4 Experiments -- 5 Conclusion -- References -- Co-restoring Multimodal Microscopy Images -- 1 Introduction -- 2 Data Acquisition -- 3 Methodology -- 3.1 Theoretical Foundation of Microscopy Image Restoration -- 3.2 Multimodal Microscopy Image Restoration Algorithm -- 3.3 Build a Look-Up Table for Better Initialization -- 3.4 Cell Segmentation and Classification Based on Co-restoration -- 4 Experimental Results -- 4.1 Qualitative Evaluation -- 4.2 Quantitative Evaluation -- 5 Conclusion -- References -- A 3D Primary Vessel Reconstruction Framework with Serial Microscopy Images -- 1 Introduction -- 2 Methods for 3D Vessel Reconstruction -- 2.1 Automated 2D Vessel Segmentation -- 2.2 Two-Stage Vessel Association with Vessel Cross-Sections -- 3 Experimental Results and Validation -- 4 Conclusion -- References -- Adaptive Co-occurrence Differential Texton Space for HEp-2 Cells Classification -- 1 Introduction -- 2 Method -- 2.1 Co-occurrence Differential Texton -- 2.2 HEp-2 Cell Image Representation in the Adaptive CoDT Feature Space -- 3 Experiments and Comparisons -- 3.1 Datasets -- 3.2 Experimental Results -- 4 Conclusion. 327 $aReferences. 330 $aThe three-volume set LNCS 9349, 9350, and 9351 constitutes the refereed proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2015, held in Munich, Germany, in October 2015. Based on rigorous peer reviews, the program committee carefully selected 263 revised papers from 810 submissions for presentation in three volumes. The papers have been organized in the following topical sections: quantitative image analysis I: segmentation and measurement; computer-aided diagnosis: machine learning; computer-aided diagnosis: automation; quantitative image analysis II: classification, detection, features, and morphology; advanced MRI: diffusion, fMRI, DCE; quantitative image analysis III: motion, deformation, development and degeneration; quantitative image analysis IV: microscopy, fluorescence and histological imagery; registration: method and advanced applications; reconstruction, image formation, advanced acquisition - computational imaging; modelling and simulation for diagnosis and interventional planning; computer-assisted and image-guided interventions. . 410 0$aImage Processing, Computer Vision, Pattern Recognition, and Graphics ;$v9351 606 $aOptical data processing 606 $aPattern recognition 606 $aComputer graphics 606 $aArtificial intelligence 606 $aRadiology 606 $aHealth informatics 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 $aImaging / Radiology$3https://scigraph.springernature.com/ontologies/product-market-codes/H29005 606 $aHealth Informatics$3https://scigraph.springernature.com/ontologies/product-market-codes/H28009 615 0$aOptical data processing. 615 0$aPattern recognition. 615 0$aComputer graphics. 615 0$aArtificial intelligence. 615 0$aRadiology. 615 0$aHealth informatics. 615 14$aImage Processing and Computer Vision. 615 24$aPattern Recognition. 615 24$aComputer Graphics. 615 24$aArtificial Intelligence. 615 24$aImaging / Radiology. 615 24$aHealth Informatics. 676 $a610.285 702 $aNavab$b Nassir$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aHornegger$b Joachim$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aWells$b William M$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aFrangi$b Alejandro$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910484376703321 996 $aMedical Image Computing and Computer-Assisted Intervention ? 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