Titlebook: Medical Image Computing and Computer Assisted Intervention – MICCAI 2018; 21st International C Alejandro F. Frangi,Julia A. Schnabel,Gabor

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A No-Reference Quality Metric for Retinal Vessel Tree Segmentationfulness of our approach by applying it to automatically find a threshold for soft probabilistic segmentations on a per-image basis. For an independent state-of-the-art unsupervised vessel segmentation technique, the thresholds selected by our approach lead to statistically significant improvements i

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Efficient and Accurate MRI Super-Resolution Using a Generative Adversarial Network and 3D Multi-leveromotes realistic output hardly distinguishable from original HR images. Our results from experiments on a dataset with 1,113 subjects shows that our new architecture outperforms other popular deep learning methods in recovering 4x resolution-downgraded images and runs 6x faster.

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A Deep Learning Based Anti-aliasing Self Super-Resolution Algorithm for MRIsuch training data are often unavailable. This paper presents an anti-aliasing?(AA) and self super-resolution?(SSR) algorithm that needs no external training data. It takes advantage of the fact that the in-plane slices of those MR images contain high frequency information. Our algorithm consists of

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Phase-Sensitive Region-of-Interest Computed Tomography, and shows high-quality results on simulated data and on a biological mouse sample. This work is a proof of concept showing the potential to use PCI in CT on large specimen, such as humans, in clinical applications.

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GRACE

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ür Studien zu Informalit?ten hervor und liefert Anregungen für zukünftige Forschungen in der Organisations- und Rechtssoziologie.978-3-658-06154-8978-3-658-06155-5Series ISSN 2570-334X Series E-ISSN 2570-3358

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