LEADER 08240nam 22007935 450 001 9910483725503321 005 20240206140740.0 010 $a3-030-68107-6 024 7 $a10.1007/978-3-030-68107-4 035 $a(CKB)4100000011743096 035 $a(DE-He213)978-3-030-68107-4 035 $a(MiAaPQ)EBC6465640 035 $a(PPN)253253187 035 $a(EXLCZ)994100000011743096 100 $a20210128d2021 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aStatistical Atlases and Computational Models of the Heart. M&Ms and EMIDEC Challenges$b[electronic resource] $e11th International Workshop, STACOM 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, Revised Selected Papers /$fedited by Esther Puyol Anton, Mihaela Pop, Maxime Sermesant, Victor Campello, Alain Lalande, Karim Lekadir, Avan Suinesiaputra, Oscar Camara, Alistair Young 205 $a1st ed. 2021. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2021. 215 $a1 online resource (XV, 417 p. 176 illus., 165 illus. in color.) 225 1 $aImage Processing, Computer Vision, Pattern Recognition, and Graphics ;$v12592 300 $aIncludes index. 311 $a3-030-68106-8 327 $aRegular papers -- A persistent homology-based topological loss function for multi-class CNN segmentation of cardiac MRI -- Automatic multiplanar CT reformatting from trans-axial into left ventricle short-axis view -- Graph convolutional regression of cardiac depolarization from sparse endocardial maps -- A cartesian grid representation of left atrial appendages for deep learning based estimation of thrombogenic risk predictors -- Measure Anatomical Thickness from Cardiac MRI with Deep Neural Networks -- Modelling Fine-rained Cardiac Motion via Spatio-temporal Graph Convolutional Networks to Boost the Diagnosis of Heart Conditions- Towards mesh-free patient-specific mitral valve modeling -- PIEMAP: Personalized Inverse Eikonal Model from cardiac Electro-Anatomical Maps -- Automatic Detection of Landmarks for Fast Cardiac MR Image Registration -- Quality-aware semi-supervised learning for CMR segmentation -- Estimation of imaging biomarker?s progression in post-infarct patients using cross-sectional data -- PC-U Net: Learning to Jointly Reconstruct and Segment the Cardiac Walls in 3D from CT Data -- Shape constrained CNN for cardiac MR segmentation with simultaneous prediction of shape and pose parameters -- Left atrial ejection fraction estimation using SEGANet for fully automated segmentation of CINE MRI -- Estimation of Cardiac Valve Annuli Motion with Deep Learning -- 4D Flow Magnetic Resonance Imaging for Left Atrial Haemodynamic Characterization and Model Calibration -- Segmentation-free Estimation of Aortic Diameters from MRI Using Deep Learning -- M&Ms challenge -- Histogram Matching Augmentation for Domain Adaptation with Application to Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Image Segmentation -- Disentangled Representations for Domain-generalized Cardiac Segmentation -- A 2-step Deep Learning method with Domain Adaptation for Multi-Centre, Multi-Vendor and Multi-Disease Cardiac Magnetic Resonance Segmentation -- Random Style Transfer based Domain Generalization Networks Integrating Shape and Spatial Information -- Semi-supervised Cardiac Image Segmentation via Label Propagation and Style Transfer -- Domain-Adversarial Learning for Multi-Centre, Multi-Vendor, and Multi-Disease Cardiac MR Image Segmentation -- Studying Robustness of Segmantic Segmentation under Domain Shift in cardiac MRI -- A deep convolutional neural network approach for the segmentation of cardiac structures from MRI sequences -- Multi-center, Multi-vendor, and Multi-disease Cardiac Image Segmentation Using Scale-Independent Multi-Gate UNET -- Adaptive Preprocessing for Generalization in Cardiac MR Image Segmentation -- Deidentifying MRI data domain by iterative backpropagation -- A generalizable deep-learning approach for cardiac magnetic resonance image segmentation using image augmentation and attention U-Net -- Generalisable Cardiac Structure Segmentation via Attentional and Stacked Image Adaptation -- Style-invariant Cardiac Image Segmentation with Test-time Augmentation -- EMIDEC challenge -- Comparison of a Hybrid Mixture Model and a CNN for the Segmentation of Myocardial Pathologies in Delayed Enhancement MRI -- Cascaded Convolutional Neural Network for Automatic Myocardial Infarction Segmentation from Delayed-Enhancement Cardiac MRI -- Automatic Myocardial Disease Prediction From Delayed-Enhancement Cardiac MRI and Clinical Information -- SM2N2: A Stacked Architecture for Multimodal Data and its Application to Myocardial Infarction Detection -- A Hybrid Network for Automatic Myocardial Infarction Segmentation in Delayed Enhancement-MRI -- Efficient 3D deep learning for myocardial diseases segmentation -- Deep-learning-based myocardial pathology detection -- Automatic Myocardial Infarction Evaluation from Delayed-Enhancement Cardiac MRI using Deep Convolutional Networks -- Uncertainty-based Segmentation of Myocardial Infarction Areas on Cardiac MR images -- Anatomy Prior Based U-net for Pathology Segmentation with Attention -- Automatic Scar Segmentation from DE-MRI Using 2D Dilated UNet with Rotation-based Augmentation -- Classification of pathological cases of myocardial infarction using Convolutional Neural Network and Random Forest. . 330 $aThis book constitutes the proceedings of the 11th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2020, as well as two challenges: M&Ms - The Multi-Centre, Multi-Vendor, Multi-Disease Segmentation Challenge, and EMIDEC - Automatic Evaluation of Myocardial Infarction from Delayed-Enhancement Cardiac MRI Challenge. The 43 full papers included in this volume were carefully reviewed and selected from 70 submissions. They deal with cardiac imaging and image processing, machine learning applied to cardiac imaging and image analysis, atlas construction, artificial intelligence, statistical modelling of cardiac function across different patient populations, cardiac computational physiology, model customization, atlas based functional analysis, ontological schemata for data and results, integrated functional and structural analyses, as well as the pre-clinical and clinical applicability of these methods. . 410 0$aImage Processing, Computer Vision, Pattern Recognition, and Graphics ;$v12592 606 $aComputer vision 606 $aMachine learning 606 $aPattern recognition systems 606 $aSocial sciences$xData processing 606 $aEducation$xData processing 606 $aComputer Vision 606 $aMachine Learning 606 $aAutomated Pattern Recognition 606 $aComputer Application in Social and Behavioral Sciences 606 $aComputers and Education 606 $aAprenentatge automàtic$2thub 606 $aIntel·ligència artificial$2thub 606 $aImatges per ressonància magnètica$2thub 606 $aMalalties cardiovasculars$2thub 608 $aCongressos$2thub 608 $aLlibres electrònics$2thub 615 0$aComputer vision. 615 0$aMachine learning. 615 0$aPattern recognition systems. 615 0$aSocial sciences$xData processing. 615 0$aEducation$xData processing. 615 14$aComputer Vision. 615 24$aMachine Learning. 615 24$aAutomated Pattern Recognition. 615 24$aComputer Application in Social and Behavioral Sciences. 615 24$aComputers and Education. 615 7$aAprenentatge automàtic 615 7$aIntel·ligència artificial 615 7$aImatges per ressonància magnètica 615 7$aMalalties cardiovasculars 676 $a621.367 702 $aPuyol Anton$b Esther 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bUtOrBLW 906 $aBOOK 912 $a9910483725503321 996 $aStatistical Atlases and Computational Models of the Heart. M&Ms and EMIDEC Challenges$93561217 997 $aUNINA