Intravascular Imaging and Computer Assisted Stenting and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis

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.<p></p>