Titlebook: Simulation and Synthesis in Medical Imaging; 6th International Wo David Svoboda,Ninon Burgos,Can Zhao Conference proceedings 2021 Springer

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The Role of MRI Physics in Brain Segmentation CNNs: Achieving Acquisition Invariance and Instructivend acquisition-parameter dependent biases. It is important therefore to design algorithms that are not only robust to images of differing contrasts, but also be able to generalise well to unseen ones, with a quantifiable measure of uncertainty. In this paper we demonstrate the efficacy of a physics-

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X-ray Synthesis Based on Triangular Mesh Models Using GPU-Accelerated Ray Tracing for Multi-modal Bran important processing step for optimizing registration parameters and deriving images for multi-modal diagnosis. A fast computation time for creating synthetic images is essential to enable a clinically relevant application. In this paper we present a method to create synthetic X-ray attenuation i

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Frozen-to-Paraffin: Categorization of Histological Frozen Sections by the Aid of Paraffin Sections aistological findings during the intervention to base intra-operative decisions on the outcome of the histology. However, compared to paraffin sections, the quality of frozen sections is typically lower, leading to a higher ratio of miss-classification. In this work, we investigated the effect of the

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GAN-Based Synthetic FDG PET Images from T1 Brain MRI Can Serve to Improve Performance of Deep Unsuperated datasets for the training of deep models, with promising performance of GAN-based architectures. However, only a few of these studies assessed task-based related performance of these synthetic data. In this work, we design and compare different GAN-based frameworks for generating synthetic bra

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Transfer Learning in Optical Microscopychniques in modern microscopy. In this paper, we propose a novel method based on DenseNet architecture and we compare it with Pix2Pix model in the task of translation from images imaged using phase-contrast technique to fluorescence images with focus on usability for cell segmentation.