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001434277 019__ $$a1235760969$$a1236260990$$a1241066029$$a1249944303
001434277 020__ $$a9783030681074$$q(electronic book)
001434277 020__ $$a3030681076$$q(electronic book)
001434277 020__ $$z3030681068
001434277 020__ $$z9783030681067
001434277 0247_ $$a10.1007/978-3-030-68107-4$$2doi
001434277 035__ $$aSP(OCoLC)1239000010
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001434277 049__ $$aISEA
001434277 050_4 $$aRC683.5.I42
001434277 08204 $$a616.1/0754$$223
001434277 1112_ $$aSTACOM (Workshop)$$n(11th :$$d2020 :$$cOnline)
001434277 24510 $$aStatistical atlases and computational models of the heart :$$bM & Ms and EMIDEC challenges : 11th International Workshop, STACOM 2020, held in conjunction with MICCAI 2020, Lima, Peru, October 4, 2020, revised selected papers /$$cEsther Puyol Anton, Mihaela Pop, Maxime Sermesant, Victor Campello, Alain Lalande, Karim Lekadir, Avan Suinesiaputra, Oscar Camara, Alistair Young (eds.).
001434277 2463_ $$aSTACOM 2020
001434277 2463_ $$aMICCAI 2020
001434277 264_1 $$aCham, Switzerland :$$bSpringer,$$c[2021]
001434277 300__ $$a1 online resource (xv, 417 pages) :$$billustrations (chiefly color)
001434277 336__ $$atext$$btxt$$2rdacontent
001434277 337__ $$acomputer$$bc$$2rdamedia
001434277 338__ $$aonline resource$$bcr$$2rdacarrier
001434277 347__ $$atext file
001434277 347__ $$bPDF
001434277 4901_ $$aLecture notes in computer science ;$$v12592
001434277 4901_ $$aLNCS sublibrary. SL 6 - Image processing, computer vision, pattern recognition, and graphics
001434277 500__ $$a"The 11th edition of theStatistical Atlases and Computational Modelling of the Heart workshop, STACOM 2020 (http://stacom2020.cardiacatlas.org), was held inconjunction with the MICCAI 2020 international conference (held virtually)"--Preface
001434277 504__ $$aIncludes bibliographical references and index.
001434277 5050_ $$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.
001434277 506__ $$aAccess limited to authorized users.
001434277 520__ $$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.
001434277 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed March 10, 2021).
001434277 650_0 $$aHeart$$xImaging$$vCongresses.
001434277 650_0 $$aImaging systems in medicine$$vCongresses.
001434277 650_0 $$aThree-dimensional imaging in medicine$$vCongresses.
001434277 650_0 $$aHeart$$xComputer simulation$$vCongresses.
001434277 650_0 $$aImage processing$$xDigital techniques$$vCongresses.
001434277 650_6 $$aCœur$$xImagerie$$vCongrès.
001434277 650_6 $$aImagerie médicale$$vCongrès.
001434277 650_6 $$aImagerie tridimensionnelle en médecine$$vCongrès.
001434277 650_6 $$aCœur$$xSimulation par ordinateur$$vCongrès.
001434277 650_6 $$aTraitement d'images$$xTechniques numériques$$vCongrès.
001434277 655_7 $$aConference papers and proceedings.$$2fast$$0(OCoLC)fst01423772
001434277 655_7 $$aConference papers and proceedings.$$2lcgft
001434277 655_7 $$aActes de congrès.$$2rvmgf
001434277 655_0 $$aElectronic books.
001434277 7001_ $$aPuyol Anton, Esther,$$eeditor.
001434277 7001_ $$aPop, Mihaela$$c(Scientist),$$eeditor.
001434277 7001_ $$aSermesant, Maxime,$$eeditor.
001434277 7001_ $$aCampello, Victor,$$eeditor.
001434277 7001_ $$aLalande, Alain,$$eeditor.
001434277 7001_ $$aLekadir, Karim,$$eeditor$$1https://orcid.org/0000-0002-9456-1612
001434277 7001_ $$aSuinesiaputra, Avan,$$eeditor.
001434277 7001_ $$aCamara, Oscar$$q(Oscar Camara Rey),$$eeditor.
001434277 7001_ $$aYoung, Alistair$$q(Alistair Andrew),$$eeditor.
001434277 7112_ $$aInternational Conference on Medical Image Computing and Computer-Assisted Intervention$$n(23rd :$$d2020 :$$cOnline),$$jjointly held conference.
001434277 77608 $$iPrint version:$$z9783030681067
001434277 830_0 $$aLecture notes in computer science ;$$v12592.
001434277 830_0 $$aLNCS sublibrary.$$nSL 6,$$pImage processing, computer vision, pattern recognition, and graphics.
001434277 852__ $$bebk
001434277 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-68107-4$$zOnline Access$$91397441.1
001434277 909CO $$ooai:library.usi.edu:1434277$$pGLOBAL_SET
001434277 980__ $$aBIB
001434277 980__ $$aEBOOK
001434277 982__ $$aEbook
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