LEADER 04356nam 22006975 450 001 9910373928703321 005 20201023203544.0 010 $a3-030-39074-8 024 7 $a10.1007/978-3-030-39074-7 035 $a(CKB)4100000010121590 035 $a(DE-He213)978-3-030-39074-7 035 $a(MiAaPQ)EBC6112676 035 $a(PPN)242844960 035 $a(EXLCZ)994100000010121590 100 $a20200122d2020 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. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges $e10th International Workshop, STACOM 2019, Held in Conjunction with MICCAI 2019, Shenzhen, China, October 13, 2019, Revised Selected Papers /$fedited by Mihaela Pop, Maxime Sermesant, Oscar Camara, Xiahai Zhuang, Shuo Li, Alistair Young, Tommaso Mansi, Avan Suinesiaputra 205 $a1st ed. 2020. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2020. 215 $a1 online resource (XV, 417 p. 200 illus., 168 illus. in color.) 225 1 $aImage Processing, Computer Vision, Pattern Recognition, and Graphics ;$v12009 311 $a3-030-39073-X 327 $aRegular Papers -- Multi-Sequence CMR Segmentation Challenge -- CRT-EPiggy Challenge -- LV Full Quantification Challenge. 330 $aThis book constitutes the thoroughly refereed post-workshop proceedings of the 10th International Workshop on Statistical Atlases and Computational Models of the Heart: Atrial Segmentation and LV Quantification Challenges, STACOM 2019, held in conjunction with MICCAI 2019, in Shenzhen, China, in October 2019. The 42 revised full workshop papers were carefully reviewed and selected from 76 submissions. The topics of the workshop included: cardiac imaging and image processing, machine learning applied to cardiac imaging and image analysis, atlas construction, statistical modelling of cardiac function across different patient populations, cardiac computational physiology, model customization, atlas based functional analysis, ontological schemata for data and results, integrated functional and structural analyses, as well as the pre-clinical and clinical applicability of these methods. 410 0$aImage Processing, Computer Vision, Pattern Recognition, and Graphics ;$v12009 606 $aOptical data processing 606 $aArtificial intelligence 606 $aPattern recognition 606 $aApplication software 606 $aImage Processing and Computer Vision$3https://scigraph.springernature.com/ontologies/product-market-codes/I22021 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aPattern Recognition$3https://scigraph.springernature.com/ontologies/product-market-codes/I2203X 606 $aComputer Applications$3https://scigraph.springernature.com/ontologies/product-market-codes/I23001 615 0$aOptical data processing. 615 0$aArtificial intelligence. 615 0$aPattern recognition. 615 0$aApplication software. 615 14$aImage Processing and Computer Vision. 615 24$aArtificial Intelligence. 615 24$aPattern Recognition. 615 24$aComputer Applications. 676 $a611.12 702 $aPop$b Mihaela$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aSermesant$b Maxime$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aCamara$b Oscar$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aZhuang$b Xiahai$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aLi$b Shuo$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aYoung$b Alistair$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aMansi$b Tommaso$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aSuinesiaputra$b Avan$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910373928703321 996 $aStatistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges$92282123 997 $aUNINA