Titlebook: Simulation and Synthesis in Medical Imaging; 7th International Wo Can Zhao,David Svoboda,Maria Escobar Conference proceedings 2022 The Edit

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,Subject-Specific Lesion Generation and Pseudo-Healthy Synthesis for Multiple Sclerosis Brain Imagesease burden and outcome. In this work, we present a novel foreground-based generative method for modelling the local lesion characteristics that can both generate synthetic lesions on healthy images and synthesize subject-specific pseudo-healthy images from pathological images. Furthermore, the prop

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,Generating Artificial Artifacts for Motion Artifact Detection in Chest CT,ures. Localising motion artifacts in the lungs can improve diagnosis quality. The diverse appearance of artifacts requires large quantities of annotations to train a detection model, but manual annotations can be subjective, unreliable, and are labour intensive to obtain. We propose a novel method (

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,Probabilistic Image Diversification to Improve Segmentation in 3D Microscopy Image Data, image data. Especially for 3D image data, generation of such annotations remains a challenge, increasing the demand for approaches making most out of existing annotations. We propose a probabilistic approach to increase image data diversity in small annotated data sets without further cost, to impr