Intravascular Imaging and Computer Assisted Stenting and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis (häftad)
Format
Häftad (Paperback / softback)
Språk
Engelska
Antal sidor
202
Utgivningsdatum
2018-10-17
Upplaga
1st ed. 2018
Förlag
Springer Nature Switzerland AG
Medarbetare
Maier-Hein, Lena (red.)/Duong, Luc (red.)/Zahnd, Guillaume (red.)/Demirci, Stefanie (red.)/Albarqouni, Shadi (red.)
Illustratör/Fotograf
Bibliographie
Illustrationer
65 Illustrations, color; 46 Illustrations, black and white; XVII, 202 p. 111 illus., 65 illus. in co
Dimensioner
234 x 156 x 12 mm
Vikt
318 g
Antal komponenter
1
Komponenter
1 Paperback / softback
ISBN
9783030013639
Intravascular Imaging and Computer Assisted Stenting and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis (häftad)

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

Häftad Engelska, 2018-10-17
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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.
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Innehållsförteckning

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.