Generation of Artificial CT Images using Patch-based Conditional Generative Adversarial Networks

• URL: http://arxiv.org/abs/2205.09842v1
• Date: Thu, 19 May 2022 20:29:25 GMT
• Title: Generation of Artificial CT Images using Patch-based Conditional Generative Adversarial Networks
• Authors: Marija Habijan, Irena Galic
• Abstract summary: We present an image generation approach that uses generative adversarial networks with a conditional discriminator. We validate the feasibility of GAN-enhanced medical image generation on whole heart computed tomography (CT) images.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Deep learning has a great potential to alleviate diagnosis and prognosis for various clinical procedures. However, the lack of a sufficient number of medical images is the most common obstacle in conducting image-based analysis using deep learning. Due to the annotations scarcity, semi-supervised techniques in the automatic medical analysis are getting high attention. Artificial data augmentation and generation techniques such as generative adversarial networks (GANs) may help overcome this obstacle. In this work, we present an image generation approach that uses generative adversarial networks with a conditional discriminator where segmentation masks are used as conditions for image generation. We validate the feasibility of GAN-enhanced medical image generation on whole heart computed tomography (CT) images and its seven substructures, namely: left ventricle, right ventricle, left atrium, right atrium, myocardium, pulmonary arteries, and aorta. Obtained results demonstrate the suitability of the proposed adversarial approach for the accurate generation of high-quality CT images. The presented method shows great potential to facilitate further research in the domain of artificial medical image generation.

Related papers

• StealthDiffusion: Towards Evading Diffusion Forensic Detection through Diffusion Model [62.25424831998405]
  StealthDiffusion is a framework that modifies AI-generated images into high-quality, imperceptible adversarial examples. It is effective in both white-box and black-box settings, transforming AI-generated images into high-quality adversarial forgeries.
  arXiv  Detail & Related papers  (2024-08-11T01:22:29Z)
• Applying Conditional Generative Adversarial Networks for Imaging Diagnosis [3.881664394416534]
  This study introduces an innovative application of Conditional Generative Adversarial Networks (C-GAN) integrated with Stacked Hourglass Networks (SHGN) We address the problem of overfitting, common in deep learning models applied to complex imaging datasets, by augmenting data through rotation and scaling. A hybrid loss function combining L1 and L2 reconstruction losses, enriched with adversarial training, is introduced to refine segmentation processes in intravascular ultrasound (IVUS) imaging.
  arXiv  Detail & Related papers  (2024-07-17T23:23:09Z)
• Generative Adversarial Networks in Ultrasound Imaging: Extending Field of View Beyond Conventional Limits [1.6588671405657123]
  TTE ultrasound imaging faces inherent limitations, notably the trade-off between field of view (FoV) and resolution. This paper introduces a novel application of conditional Generative Adversarial Networks (cGANs) Our proposed cGAN architecture, termed echoGAN, demonstrates the capability to generate realistic anatomical structures through outpainting.
  arXiv  Detail & Related papers  (2024-05-31T16:26:30Z)
• COIN: Counterfactual inpainting for weakly supervised semantic segmentation for medical images [3.5418498524791766]
  This research is development of a novel counterfactual inpainting approach (COIN) COIN flips the predicted classification label from abnormal to normal by using a generative model. The effectiveness of the method is demonstrated by segmenting synthetic targets and actual kidney tumors from CT images acquired from Tartu University Hospital in Estonia.
  arXiv  Detail & Related papers  (2024-04-19T12:09:49Z)
• MITS-GAN: Safeguarding Medical Imaging from Tampering with Generative Adversarial Networks [48.686454485328895]
  This study introduces MITS-GAN, a novel approach to prevent tampering in medical images. The approach disrupts the output of the attacker's CT-GAN architecture by introducing finely tuned perturbations that are imperceptible to the human eye. Experimental results on a CT scan demonstrate MITS-GAN's superior performance.
  arXiv  Detail & Related papers  (2024-01-17T22:30:41Z)
• Harmonizing Pathological and Normal Pixels for Pseudo-healthy Synthesis [68.5287824124996]
  We present a new type of discriminator, the segmentor, to accurately locate the lesions and improve the visual quality of pseudo-healthy images. We apply the generated images into medical image enhancement and utilize the enhanced results to cope with the low contrast problem. Comprehensive experiments on the T2 modality of BraTS demonstrate that the proposed method substantially outperforms the state-of-the-art methods.
  arXiv  Detail & Related papers  (2022-03-29T08:41:17Z)
• AI-Enabled Ultra-Low-Dose CT Reconstruction [8.135337706680097]
  In this paper, we demonstrate that AI-powered CT reconstruction offers diagnostic image quality at an ultra-low-dose level comparable to that of radiography. The reconstruction results from clinical datasets show that excellent images can be reconstructed using SUGAR from 36 projections.
  arXiv  Detail & Related papers  (2021-06-17T22:13:11Z)
• Pathology-Aware Generative Adversarial Networks for Medical Image Augmentation [0.22843885788439805]
  Generative Adversarial Networks (GANs) can generate realistic but novel samples, and thus effectively cover the real image distribution. This thesis contains four GAN projects aiming to present such novel applications' clinical relevance in collaboration with physicians.
  arXiv  Detail & Related papers  (2021-06-03T15:08:14Z)
• Generative Adversarial U-Net for Domain-free Medical Image Augmentation [49.72048151146307]
  The shortage of annotated medical images is one of the biggest challenges in the field of medical image computing. In this paper, we develop a novel generative method named generative adversarial U-Net. Our newly designed model is domain-free and generalizable to various medical images.
  arXiv  Detail & Related papers  (2021-01-12T23:02:26Z)
• Explaining Clinical Decision Support Systems in Medical Imaging using Cycle-Consistent Activation Maximization [112.2628296775395]
  Clinical decision support using deep neural networks has become a topic of steadily growing interest. clinicians are often hesitant to adopt the technology because its underlying decision-making process is considered to be intransparent and difficult to comprehend. We propose a novel decision explanation scheme based on CycleGAN activation which generates high-quality visualizations of classifier decisions even in smaller data sets.
  arXiv  Detail & Related papers  (2020-10-09T14:39:27Z)
• Design and Development of a Web-based Tool for Inpainting of Dissected Aortae in Angiography Images [69.14026408176609]
  The proposed inpainting tool combines a neural network, which was trained on the task of inpainting aortic dissections. By designing the tool as a web application, we simplify the usage of the neural network and reduce the initial learning curve.
  arXiv  Detail & Related papers  (2020-05-06T12:22:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.