LEADER 07243nam 22008415 450 001 9910349396903321 005 20200902193219.0 010 $a3-030-01364-2 024 7 $a10.1007/978-3-030-01364-6 035 $a(CKB)4100000007110810 035 $a(DE-He213)978-3-030-01364-6 035 $a(MiAaPQ)EBC6284819 035 $a(PPN)231460767 035 $a(EXLCZ)994100000007110810 100 $a20181016d2018 u| 0 101 0 $aeng 135 $aurcn#nnn||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aIntravascular Imaging and Computer Assisted Stenting and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis$b[electronic resource] $e7th Joint International Workshop, CVII-STENT 2018 and Third International Workshop, LABELS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings /$fedited by Danail Stoyanov, Zeike Taylor, Simone Balocco, Raphael Sznitman, Anne Martel, Lena Maier-Hein, Luc Duong, Guillaume Zahnd, Stefanie Demirci, Shadi Albarqouni, Su-Lin Lee, Stefano Moriconi, Veronika Cheplygina, Diana Mateus, Emanuele Trucco, Eric Granger, Pierre Jannin 205 $a1st ed. 2018. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2018. 215 $a1 online resource (xvii, 202 pages) $ccolor illustrations 225 1 $aImage Processing, Computer Vision, Pattern Recognition, and Graphics ;$v11043 300 $aIncludes index. 320 $aIncludes bibliographical references and index. 327 $aBlood-flow estimation in the hepatic arteries based on 3D/2D angiography registration -- Automated quantification of blood flow velocity from time-resolved CT angiography -- Multiple device segmentation for fluoroscopic imaging using multi-task learning -- Segmentation of the Aorta Using Active Contours with Histogram-Based Descriptors -- Layer Separation in X-ray Angiograms for Vessel Enhancement with Fully Convolutional Network -- Generation of a HER2 breast cancer gold-standard using supervised learning from multiple experts -- Deep Learning-based Detection and Segmentation for BVS Struts in IVOCT Images -- Towards Automatic Measurement of Type B Aortic Dissection Parameters -- Prediction of FFR from IVUS Images using Machine Learning -- Deep Learning Retinal Vessel Segmentation From a Single Annotated Example: An Application of Cyclic Generative Adversarial Neural Networks -- An Efficient and Comprehensive Labeling Tool for Large-scale Annotation of Fundus Images -- Crowd disagreement about medical images is informative -- Imperfect Segmentation Labels: How Much Do They Matter? -- Crowdsourcing annotation of surgical instruments in videos of cataract surgery -- Four-dimensional ASL MR angiography phantoms with noise learned by neural styling -- Feature learning based on visual similarity triplets in medical image analysis: A case study of emphysema in chest CT scans -- Capsule Networks against Medical Imaging Data Challenges -- Fully Automatic Segmentation of Coronary Arteries based on Deep Neural Network in Intravascular Ultrasound Images -- Weakly-Supervised Learning for Tool Localization in Laparoscopic Videos -- Radiology Objects in COntext (ROCO) -- Improving out-of-sample prediction of quality of MRIQC. 330 $aThis book constitutes the refereed joint proceedings of the 7th Joint International Workshop on Computing and Visualization for Intravascular Imaging and Computer Assisted Stenting, CVII-STENT 2018, and the Third International Workshop on Large-Scale Annotation of Biomedical Data and Expert Label Synthesis, LABELS 2018, held in conjunction with the 21th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, in Granada, Spain, in September 2018. The 9 full papers presented at CVII-STENT 2017 and the 12 full papers presented at LABELS 2017 were carefully reviewed and selected. The CVII-STENT papers feature the state of the art in imaging, treatment, and computer-assisted intervention in the field of endovascular interventions. The LABELS papers present a variety of approaches for dealing with few labels, from transfer learning to crowdsourcing. 410 0$aImage Processing, Computer Vision, Pattern Recognition, and Graphics ;$v11043 606 $aOptical data processing 606 $aHealth informatics 606 $aArtificial intelligence 606 $aComputer organization 606 $aImage Processing and Computer Vision$3https://scigraph.springernature.com/ontologies/product-market-codes/I22021 606 $aHealth Informatics$3https://scigraph.springernature.com/ontologies/product-market-codes/I23060 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aComputer Systems Organization and Communication Networks$3https://scigraph.springernature.com/ontologies/product-market-codes/I13006 615 0$aOptical data processing. 615 0$aHealth informatics. 615 0$aArtificial intelligence. 615 0$aComputer organization. 615 14$aImage Processing and Computer Vision. 615 24$aHealth Informatics. 615 24$aArtificial Intelligence. 615 24$aComputer Systems Organization and Communication Networks. 676 $a651.504261 702 $aStoyanov$b Danail$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aTaylor$b Zeike$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aBalocco$b Simone$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aSznitman$b Raphael$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aMartel$b Anne$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aMaier-Hein$b Lena$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aDuong$b Luc$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aZahnd$b Guillaume$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aDemirci$b Stefanie$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aAlbarqouni$b Shadi$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aLee$b Su-Lin$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aMoriconi$b Stefano$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aCheplygina$b Veronika$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aMateus$b Diana$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aTrucco$b Emanuele$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aGranger$b Eric$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aJannin$b Pierre$4edt$4http://id.loc.gov/vocabulary/relators/edt 712 12$aLABELS (Workshop)$d(2nd :$f2017 :$eQue?bec, Que?bec) 712 12$aInternational Conference on Medical Image Computing and Computer-Assisted Intervention$d(20th :$f2017 :$eQue?bec, Que?bec) 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910349396903321 996 $aIntravascular Imaging and Computer Assisted Stenting and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis$92214290 997 $aUNINA