Details

Autor(en) / Beteiligte

• CVII-STENT (Workshop)
• Stoyanov, Danail[Herausgeber]
• Taylor, Zeike[Herausgeber]
• Balocco, Simone[Herausgeber]
• Sznitman, Raphael[Herausgeber]
• Martel, Anne[Herausgeber]
• Maier-Hein, Lena[Herausgeber]
• Duong, Luc[Herausgeber]
• Zahnd, Guillaume[Herausgeber]
• Demirci, Stefanie[Herausgeber]
• Albarqouni, Shadi[Herausgeber]
• Lee, Su-Lin[Herausgeber]
• Moriconi, Stefano[Herausgeber]
• Cheplygina, Veronika[Herausgeber]
• Mateus, Diana[Herausgeber]
• Trucco, Emanuele[Herausgeber]
• Granger, Eric[Herausgeber]
• Jannin, Pierre[Herausgeber]

Titel

Intravascular Imaging and Computer Assisted Stenting and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis : 7th Joint International Workshop, CVII-STENT 2018 and Third International Workshop, LABELS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings [electronic resource]

Auflage

1st ed. 2018

Ort / Verlag

Cham : Springer International Publishing

Erscheinungsjahr

2018

Link zum Volltext

• SpringerLink Books - AutoHoldings

Beschreibungen/Notizen

• Includes index.
• Includes bibliographical references and index.
• Blood-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.
• This 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.