Synthesis of CT images from digital body phantoms using CycleGAN

Russ, Tom; Goerttler, Stephan; Schnurr, Alena-Kathrin; Bauer, Dominik; Hatamikia, Sepideh; Schad, Lothar R.; Zöllner, Frank G.; Chung, Khanlian

doi:10.1007/s11548-019-02042-9

Cited by 44 publications

(37 citation statements)

References 21 publications

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“…With the scarcity of annotated medical image datasets, there has been a surge of interest in developing efficient approaches for the generation of synthetic medical images. While several existing generative methods have addressed the translation between multiple imaging modalities CT-PET, CS-MRI, MR-CT, XCAT-CT (Ben- Cohen et al 2017;Yang et al 2017;Wolterink et al 2017;Russ et al 2019) based on distribution matching, other approaches have focused on the scarcity of labeled data in the medical field due in large part to the acquisition, privacy, and health safety issues. Conditional and unconditional image synthesis Fig.…”

Section: Related Workmentioning

confidence: 99%

Synthesis of COVID-19 chest X-rays using unpaired image-to-image translation

Zunair

Hamza

2021

Soc. Netw. Anal. Min.

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Motivated by the lack of publicly available datasets of chest radiographs of positive patients with coronavirus disease 2019 (COVID-19), we build the first-of-its-kind open dataset of synthetic COVID-19 chest X-ray images of high fidelity using an unsupervised domain adaptation approach by leveraging class conditioning and adversarial training. Our contributions are twofold. First, we show considerable performance improvements on COVID-19 detection using various deep learning architectures when employing synthetic images as additional training set. Second, we show how our image synthesis method can serve as a data anonymization tool by achieving comparable detection performance when trained only on synthetic data. In addition, the proposed data generation framework offers a viable solution to the COVID-19 detection in particular, and to medical image classification tasks in general. Our publicly available benchmark dataset ( https://github.com/hasibzunair/synthetic-covid-cxr-dataset. ) consists of 21,295 synthetic COVID-19 chest X-ray images. The insights gleaned from this dataset can be used for preventive actions in the fight against the COVID-19 pandemic.

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Section: Related Workmentioning

confidence: 99%

Synthesis of COVID-19 chest X-rays using unpaired image-to-image translation

Zunair

Hamza

2021

Soc. Netw. Anal. Min.

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“…Two additional networks, called discriminators, aim to distinguish between real images and generated images. Russ et al recently showed an approach to generate synthetic CT data sets to train a DL network for vessel segmentation using cGANS [73]. Tanner et al proposed CT to MR image registration using cGANs [74].…”

Section: Deep Learning Approachmentioning

confidence: 99%

Image registration in dynamic renal MRI—current status and prospects

Zöllner

Serifovic-Trbalic

Kabelitz

et al. 2019

Magn Reson Mater Phy

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Magnetic resonance imaging (MRI) modalities have achieved an increasingly important role in the clinical work-up of chronic kidney diseases (CKD). This comprises among others assessment of hemodynamic parameters by arterial spin labeling (ASL) or dynamic contrast-enhanced (DCE-) MRI. Especially in the latter, images or volumes of the kidney are acquired over time for up to several minutes. Therefore, they are hampered by motion, e.g., by pulsation, peristaltic, or breathing motion. This motion can hinder subsequent image analysis to estimate hemodynamic parameters like renal blood flow or glomerular filtration rate (GFR). To overcome motion artifacts in time-resolved renal MRI, a wide range of strategies have been proposed. Renal image registration approaches could be grouped into (1) image acquisition techniques, (2) post-processing methods, or (3) a combination of image acquisition and post-processing approaches. Despite decades of progress, the translation in clinical practice is still missing. The aim of the present article is to discuss the existing literature on renal image registration techniques and show today’s limitations of the proposed techniques that hinder clinical translation. This paper includes transformation, criterion function, and search types as traditional components and emerging registration technologies based on deep learning. The current trend points towards faster registrations and more accurate results. However, a standardized evaluation of image registration in renal MRI is still missing.

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“…[55][56][57] Recently, some studies have showed that deep learning approaches, such as generative adversarial network (GAN) models, can synthesize images that have similar visual and statistical features of a set of training input data. 58,59 These techniques can also be utilized for the purpose of modeling intraorgan heterogeneities within the organs and structures of computational phantoms, particularly for the parenchymal regions where organs usually have "textural" appearances. For example, Fig.…”

Section: Modeling Intraorgan Structuresmentioning

confidence: 99%

Virtual clinical trials in medical imaging: a review

et al. 2020

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The accelerating complexity and variety of medical imaging devices and methods have outpaced the ability to evaluate and optimize their design and clinical use. This is a significant and increasing challenge for both scientific investigations and clinical applications. Evaluations would ideally be done using clinical imaging trials. These experiments, however, are often not practical due to ethical limitations, expense, time requirements, or lack of ground truth. Virtual clinical trials (VCTs) (also known as in silico imaging trials or virtual imaging trials) offer an alternative means to efficiently evaluate medical imaging technologies virtually. They do so by simulating the patients, imaging systems, and interpreters. The field of VCTs has been constantly advanced over the past decades in multiple areas. We summarize the major developments and current status of the field of VCTs in medical imaging. We review the core components of a VCT: computational phantoms, simulators of different imaging modalities, and interpretation models. We also highlight some of the applications of VCTs across various imaging modalities.

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Synthesis of CT images from digital body phantoms using CycleGAN

Cited by 44 publications

References 21 publications

Synthesis of COVID-19 chest X-rays using unpaired image-to-image translation

Synthesis of COVID-19 chest X-rays using unpaired image-to-image translation

Image registration in dynamic renal MRI—current status and prospects

Virtual clinical trials in medical imaging: a review

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