ArticleInternational journal of computer assisted radiology and surgery2019
CycleGAN for style transfer in X-ray angiography.
Article in International journal of computer assisted radiology and surgery, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
9 citing papers in PubMed, 37 citations in OpenAlex.
- Enhancing Radiographic Diagnosis: CycleGAN-Based Methods for Reducing Cast Shadow Artifacts in Wrist Radiographs.Journal of imaging informatics in medicine · 2025Article
- Enhancing frozen histological section images using permanent-section-guided deep learning with nuclei attention.Scientific reports · 2025Article
- Generating Synthetic Data for Medical Imaging.Radiology · 2024Review
- Unsupervised synthesis of realistic coronary artery X-ray angiogram.International journal of computer assisted radiology and surgery · 2023Article
- Style Transfer-assisted Deep Learning Method for Kidney Segmentation at Multiphase MRI.Radiology. Artificial intelligence · 2023Article
- Unsupervised-learning-based method for chest MRI-CT transformation using structure constrained unsupervised generative attention networks.Scientific reports · 2022Article
- Generation of annotated multimodal ground truth datasets for abdominal medical image registration.International journal of computer assisted radiology and surgery · 2021Article
- Real-Time Cerebral Vessel Segmentation in Laser Speckle Contrast Image Based on Unsupervised Domain Adaptation.Frontiers in neuroscience · 2021Article
- pix2xray: converting RGB images into X-rays using generative adversarial networks.International journal of computer assisted radiology and surgery · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeWe aim to perform generation of angiograms for various vascular structures as a mean of data augmentation in learning tasks. The task is to enhance the realism of vessels images generated from an anatomically realistic cardiorespiratory simulator to make them look like real angiographies.
methodsThe enhancement is performed by applying the CycleGAN deep network for transferring the style of real angiograms acquired during percutaneous interventions into a data set composed of realistically simulated arteries.
resultsThe cycle consistency was evaluated by comparing an input simulated image with the one obtained after two cycles of image translation. An average structural similarity (SSIM) of 0.948 on our data sets has been obtained. The vessel preservation was measured by comparing segmentations of an input image and its corresponding enhanced image using Dice coefficient.
conclusionsWe proposed an application of the CycleGAN deep network for enhancing the artificial data as an alternative to classical data augmentation techniques for medical applications, particularly focused on angiogram generation. We discussed success and failure cases, explaining conditions for the realistic data augmentation which respects both the complex physiology of arteries and the various patterns and textures generated by X-ray angiography.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.