Toothpix: Pixel-Level Tooth Segmentation in Panoramic X-Ray Images based on Generative Adversarial Networks

2021 ◽  
Author(s):  
Weiwei Cui ◽  
Liaoyuan Zeng ◽  
Bunsan Chong ◽  
Qianni Zhang
Keyword(s):  
Generative Adversarial Networks ◽  
X Ray ◽  
Adversarial Networks ◽  
Tooth Segmentation

scholarly journals An Adaptive Generative Adversarial Network for Cardiac Segmentation from X-ray Chest Radiographs

2020 ◽  
Vol 10 (15) ◽  
pp. 5032
Author(s):  
Xiaochang Wu ◽  
Xiaolin Tian
Keyword(s):  
Image Segmentation ◽  
Medical Image Segmentation ◽  
Chest Radiographs ◽  
Generative Adversarial Networks ◽  
Generative Adversarial Network ◽  
X Ray ◽  
Adversarial Network ◽  
Cardiac Segmentation ◽  
Adversarial Networks ◽  
Feature Extractor

Medical image segmentation is a classic challenging problem. The segmentation of parts of interest in cardiac medical images is a basic task for cardiac image diagnosis and guided surgery. The effectiveness of cardiac segmentation directly affects subsequent medical applications. Generative adversarial networks have achieved outstanding success in image segmentation compared with classic neural networks by solving the oversegmentation problem. Cardiac X-ray images are prone to weak edges, artifacts, etc. This paper proposes an adaptive generative adversarial network for cardiac segmentation to improve the segmentation rate of X-ray images by generative adversarial networks. The adaptive generative adversarial network consists of three parts: a feature extractor, a discriminator and a selector. In this method, multiple generators are trained in the feature extractor. The discriminator scores the features of different dimensions. The selector selects the appropriate features and adjusts the network for the next iteration. With the help of the discriminator, this method uses multinetwork joint feature extraction to achieve network adaptivity. This method allows features of multiple dimensions to be combined to perform joint training of the network to enhance its generalization ability. The results of cardiac segmentation experiments on X-ray chest radiographs show that this method has higher segmentation accuracy and less overfitting than other methods. In addition, the proposed network is more stable.

scholarly journals Data Augmentation of X-Ray Images in Baggage Inspection Based on Generative Adversarial Networks

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 86536-86544 ◽  
Author(s):  
Yue Zhu ◽  
Yutao Zhang ◽  
Haigang Zhang ◽  
Jinfeng Yang ◽  
Zihao Zhao
Keyword(s):  
Data Augmentation ◽  
Generative Adversarial Networks ◽  
X Ray ◽  
Adversarial Networks

scholarly journals Data Augmentation for X-Ray Prohibited Item Images Using Generative Adversarial Networks

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 28894-28902 ◽  
Author(s):  
Jinfeng Yang ◽  
Zihao Zhao ◽  
Haigang Zhang ◽  
Yihua Shi
Keyword(s):  
Data Augmentation ◽  
Generative Adversarial Networks ◽  
X Ray ◽  
Adversarial Networks

scholarly journals Segmentation of Lungs in Chest X-Ray Image Using Generative Adversarial Networks

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 153535-153545
Author(s):  
Faizan Munawar ◽  
Shoaib Azmat ◽  
Talha Iqbal ◽  
Christer Gronlund ◽  
Hazrat Ali
Keyword(s):  
Generative Adversarial Networks ◽  
X Ray ◽  
Adversarial Networks ◽  
Chest X Ray

scholarly journals Medical Image Synthesis using Computer Vision Techniques

2020 ◽  
Vol 9 (3) ◽  
pp. 16-20
Keyword(s):  
Image Synthesis ◽  
Ct Images ◽  
The Body ◽  
Generative Adversarial Networks ◽  
Radio Waves ◽  
X Ray ◽  
Adversarial Networks ◽  
Steady Magnetic Field ◽  
Magnetic Resonance Imaging Mri ◽  
Scan Conversion

Magnetic Resonance Imaging (MRI) is a type of scan that produces comprehensive images of the inside of the body using a steady magnetic field and radio waves. On the other hand, Computed Tomography (CT) scans, is a combination of a series of X-ray images, which are a type of radiation called ionizing radiation. It can be harmful to the DNA in your cells and also increase the chances that they'll turn cancerous. MRI is a safer option compared to CT and does not involve any radiation exposure. In this paper, we propose the use of Generative Adversarial Networks (GANs) to translate MRI images into equivalent CT images. We compare it with past techniques of MRI to CT scan conversion and elaborate on why GANs produce more realistic CT images while modeling the nonlinear relationship from MRI to CT.

scholarly journals A Multi-Stage GAN for Multi-Organ Chest X-ray Image Generation and Segmentation

Mathematics ◽  
2021 ◽  
Vol 9 (22) ◽  
pp. 2896
Author(s):  
Giorgio Ciano ◽  
Paolo Andreini ◽  
Tommaso Mazzierli ◽  
Monica Bianchini ◽  
Franco Scarselli
Keyword(s):  
Data Augmentation ◽  
Semantic Segmentation ◽  
Generative Adversarial Networks ◽  
Radiographic Images ◽  
X Ray ◽  
Adversarial Networks ◽  
Multi Stage ◽  
Segmentation Methods ◽  
Chest X Ray ◽  
Time Required

Multi-organ segmentation of X-ray images is of fundamental importance for computer aided diagnosis systems. However, the most advanced semantic segmentation methods rely on deep learning and require a huge amount of labeled images, which are rarely available due to both the high cost of human resources and the time required for labeling. In this paper, we present a novel multi-stage generation algorithm based on Generative Adversarial Networks (GANs) that can produce synthetic images along with their semantic labels and can be used for data augmentation. The main feature of the method is that, unlike other approaches, generation occurs in several stages, which simplifies the procedure and allows it to be used on very small datasets. The method was evaluated on the segmentation of chest radiographic images, showing promising results. The multi-stage approach achieves state-of-the-art and, when very few images are used to train the GANs, outperforms the corresponding single-stage approach.

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