Simulation and Synthesis in Medical Imaging

6th International Workshop, SASHIMI 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings

Häftad, Engelska, 2021

Av David Svoboda, Ninon Burgos, Jelmer M. Wolterink, Can Zhao

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This book constitutes the refereed proceedings of the 6th International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2021, held in conjunction with MICCAI 2021, in Strasbourg, France, in September 2021.*The 14 full papers presented were carefully reviewed and selected from 18 submissions.

Produktinformation

Utgivningsdatum2021-09-21
Mått155 x 235 x 10 mm
Vikt265 g
FormatHäftad
SpråkEngelska
SerieLecture Notes in Computer Science
Antal sidor154
FörlagSpringer Nature Switzerland AG
ISBN9783030875916

Tillhör följande kategorier

Systemvetenskap och AI inom Data och it
Biovetenskap inom Naturvetenskap och teknik
Artificiell intelligens inom Data och it

Method-Oriented Papers.- Detail matters: high-frequency content for realistic synthetic brain MRI generation.- Joint Image and Label Self-Super-Resolution.- Super-resolution by Latent Space Exploration: Training with Poorly-aligned Clinical and Micro CT Image Dataset.- A Glimpse into the Future: Disease Progression Simulation for Breast Cancer in Mammograms.- Synth-by-Reg (SbR): Contrastive learning for synthesis-based registration of paired images.- Learning-based Template Synthesis For Groupwise Image Registration.- The role of MRI physics in brain segmentation CNNs: achieving acquisition invariance and instructive uncertainties.- Transfer Learning in Optical Microscopy.- X-ray synthesis based on triangular mesh models using GPU-accelerated ray tracing for multi-modal breast image registration.- Application-Oriented Papers.- Frozen-to-Paraffin: Categorization of Histological Frozen Sections by the Aid of Paraffin Sections and Generative Adversarial Networks.- SequenceGAN: Generating Fundus Fluorescence Angiography Sequences from Structure Fundus Image .- Cerebral Blood Volume Prediction based on Multi-modality Magnetic Resonance Imaging.- Cine-MRI simulation to evaluate tumor tracking.- GAN-based synthetic FDG PET images from T1 brain MRI can serve to improve performance of deep unsupervised anomaly detection models.