Titlebook: Simulation and Synthesis in Medical Imaging; Third International Ali Gooya,Orcun Goksel,Ninon Burgos Conference proceedings 2018 Springer

步履蹒跚

Cross-Modality Image Synthesis from Unpaired Data Using CycleGAN,oft tissue contrast. However, MRI has poor contrast for bone structures. Clearly, it would be helpful if a corresponding CT were available, as bone boundaries are more clearly seen and CT has a standardized (i.e., Hounsfield) unit. Therefore, we aim at MR-to-CT synthesis. While the CycleGAN was succ

Ergots

误传

Unsupervised Learning for Cross-Domain Medical Image Synthesis Using Deformation Invariant Cycle Con-specific nonlinear deformations captured by CycleGAN make the synthesized images difficult to be used for some applications, for example, generating pseudo-CT for PET-MR attenuation correction. This paper presents a deformation-invariant CycleGAN (DicycleGAN) method using deformable convolutional

maladorit

lactic

Model-Based Generation of Synthetic 3D Time-Lapse Sequences of Multiple Mutually Interacting Motileich hinders objective benchmarking of bioimage analysis workflows as well as training of widespread deep-learning-based approaches. In this paper, we present a novel simulation system capable of generating synthetic 3D time-lapse sequences of multiple mutually interacting cells with filopodial protr

啪心儿跳动

闪光东本

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隐藏

Lung Nodule Synthesis Using CNN-Based Latent Data Representation,large amounts of annotated training data. When it comes to Medical Imaging, annotation is often complicated and/or expensive, and innovative methods for dealing with small or very imbalanced training sets are mostly welcome. In this context, this paper proposes a novel approach for efficiently synth

Callus