scholarly journals Study on Optimal Generative Network for Synthesizing Brain Tumor-Segmented MR Images

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Hyunhee Lee ◽  
Jaechoon Jo ◽  
Heuiseok Lim
Keyword(s):  
Brain Tumor ◽  
Medical Imaging ◽  
Image Synthesis ◽  
Generative Adversarial Networks ◽  
Imaging Data ◽  
Mr Images ◽  
Style Transfer ◽  
Adversarial Networks ◽  
Robust Model ◽  
Privacy Issues

Due to institutional and privacy issues, medical imaging researches are confronted with serious data scarcity. Image synthesis using generative adversarial networks provides a generic solution to the lack of medical imaging data. We synthesize high-quality brain tumor-segmented MR images, which consists of two tasks: synthesis and segmentation. We performed experiments with two different generative networks, the first using the ResNet model, which has significant advantages of style transfer, and the second, the U-Net model, one of the most powerful models for segmentation. We compare the performance of each model and propose a more robust model for synthesizing brain tumor-segmented MR images. Although ResNet produced better-quality images than did U-Net for the same samples, it used a great deal of memory and took much longer to train. U-Net, meanwhile, segmented the brain tumors more accurately than did ResNet.

GP-GAN: Brain tumor growth prediction using stacked 3D generative adversarial networks from longitudinal MR Images

2020 ◽  
Vol 132 ◽  
pp. 321-332
Author(s):  
Ahmed Elazab ◽  
Changmiao Wang ◽  
Syed Jamal Safdar Gardezi ◽  
Hongmin Bai ◽  
Qingmao Hu ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Tumor Growth ◽  
Generative Adversarial Networks ◽  
Mr Images ◽  
Growth Prediction ◽  
Adversarial Networks

scholarly journals Unpaired Image- to- Image Translation using Cycle Generative Adversarial Networks

2020 ◽  
Vol 9 (6) ◽  
pp. 380-385
Keyword(s):  
Mapping Function ◽  
Image Synthesis ◽  
Input Image ◽  
General Purpose ◽  
Training Dataset ◽  
Generative Adversarial Networks ◽  
Inverse Mapping ◽  
Style Transfer ◽  
Adversarial Networks ◽  
Image Translation

In this burgeoning age and society where people are tending towards learning the benefits adversarial network we hereby benefiting the society tend to extend our research towards adversarial networks as a general-purpose solution to image-to-image translation problems. Image to image translation comes under the peripheral class of computer sciences extending our branch in the field of neural networks. We apprentice Generative adversarial networks as an optimum solution for generating Image to image translation where our motive is to learn a mapping between an input image(X) and an output image(Y) using a set of predefined pairs[4]. But it is not necessary that the paired dataset is provided to for our use and hence adversarial methods comes into existence. Further, we advance a method that is able to convert and recapture an image from a domain X to another domain Y in the absence of paired datasets. Our objective is to learn a mapping function G: A —B such that the mapping is able to distinguish the images of G(A) within the distribution of B using an adversarial loss.[1] Because this mapping is high biased, we introduce an inverse mapping function F B—A and introduce a cycle consistency loss[7]. Furthermore we wish to extend our research with various domains and involve them with neural style transfer, semantic image synthesis. Our essential commitment is to show that on a wide assortment of issues, conditional GANs produce sensible outcomes. This paper hence calls for the attention to the purpose of converting image X to image Y and we commit to the transfer learning of training dataset and optimising our code.You can find the source code for the same here.

scholarly journals Data augmentation using generative adversarial networks (CycleGAN) to improve generalizability in CT segmentation tasks

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Veit Sandfort ◽  
Ke Yan ◽  
Perry J. Pickhardt ◽  
Ronald M. Summers
Keyword(s):  
Medical Imaging ◽  
Data Augmentation ◽  
Generative Adversarial Networks ◽  
Distribution Data ◽  
Imaging Data ◽  
Adversarial Networks ◽  
Original Dataset ◽  
Augmentation Techniques ◽  
Ct Segmentation ◽  
Contrast Ct

AbstractLabeled medical imaging data is scarce and expensive to generate. To achieve generalizable deep learning models large amounts of data are needed. Standard data augmentation is a method to increase generalizability and is routinely performed. Generative adversarial networks offer a novel method for data augmentation. We evaluate the use of CycleGAN for data augmentation in CT segmentation tasks. Using a large image database we trained a CycleGAN to transform contrast CT images into non-contrast images. We then used the trained CycleGAN to augment our training using these synthetic non-contrast images. We compared the segmentation performance of a U-Net trained on the original dataset compared to a U-Net trained on the combined dataset of original data and synthetic non-contrast images. We further evaluated the U-Net segmentation performance on two separate datasets: The original contrast CT dataset on which segmentations were created and a second dataset from a different hospital containing only non-contrast CTs. We refer to these 2 separate datasets as the in-distribution and out-of-distribution datasets, respectively. We show that in several CT segmentation tasks performance is improved significantly, especially in out-of-distribution (noncontrast CT) data. For example, when training the model with standard augmentation techniques, performance of segmentation of the kidneys on out-of-distribution non-contrast images was dramatically lower than for in-distribution data (Dice score of 0.09 vs. 0.94 for out-of-distribution vs. in-distribution data, respectively, p < 0.001). When the kidney model was trained with CycleGAN augmentation techniques, the out-of-distribution (non-contrast) performance increased dramatically (from a Dice score of 0.09 to 0.66, p < 0.001). Improvements for the liver and spleen were smaller, from 0.86 to 0.89 and 0.65 to 0.69, respectively. We believe this method will be valuable to medical imaging researchers to reduce manual segmentation effort and cost in CT imaging.

scholarly journals Enhanced Magnetic Resonance Image Synthesis with Contrast-Aware Generative Adversarial Networks

2021 ◽  
Vol 7 (8) ◽  
pp. 133
Author(s):  
Jonas Denck ◽  
Jens Guehring ◽  
Andreas Maier ◽  
Eva Rothgang
Keyword(s):  
Magnetic Resonance ◽  
Image Synthesis ◽  
Generative Adversarial Networks ◽  
Data Generation ◽  
Mr Images ◽  
Generative Adversarial Network ◽  
Radiology Training ◽  
Repetition Time ◽  
Echo Time ◽  
Image Orientation

A magnetic resonance imaging (MRI) exam typically consists of the acquisition of multiple MR pulse sequences, which are required for a reliable diagnosis. With the rise of generative deep learning models, approaches for the synthesis of MR images are developed to either synthesize additional MR contrasts, generate synthetic data, or augment existing data for AI training. While current generative approaches allow only the synthesis of specific sets of MR contrasts, we developed a method to generate synthetic MR images with adjustable image contrast. Therefore, we trained a generative adversarial network (GAN) with a separate auxiliary classifier (AC) network to generate synthetic MR knee images conditioned on various acquisition parameters (repetition time, echo time, and image orientation). The AC determined the repetition time with a mean absolute error (MAE) of 239.6 ms, the echo time with an MAE of 1.6 ms, and the image orientation with an accuracy of 100%. Therefore, it can properly condition the generator network during training. Moreover, in a visual Turing test, two experts mislabeled 40.5% of real and synthetic MR images, demonstrating that the image quality of the generated synthetic and real MR images is comparable. This work can support radiologists and technologists during the parameterization of MR sequences by previewing the yielded MR contrast, can serve as a valuable tool for radiology training, and can be used for customized data generation to support AI training.

scholarly journals mustGAN: multi-stream Generative Adversarial Networks for MR Image Synthesis

2021 ◽  
pp. 101944
Author(s):  
Mahmut Yurt ◽  
Salman U.H. Dar ◽  
Aykut Erdem ◽  
Erkut Erdem ◽  
Kader K Oguz ◽  
...  
Keyword(s):  
Image Synthesis ◽  
Generative Adversarial Networks ◽  
Mr Image ◽  
Adversarial Networks

scholarly journals Three-Dimensional Liver Image Segmentation Using Generative Adversarial Networks Based on Feature Restoration

2022 ◽  
Vol 8 ◽  
Author(s):  
Runnan He ◽  
Shiqi Xu ◽  
Yashu Liu ◽  
Qince Li ◽  
Yang Liu ◽  
...  
Keyword(s):  
Medical Imaging ◽  
Random Noise ◽  
Three Dimensional ◽  
Poor Quality ◽  
Training Data ◽  
Generative Adversarial Networks ◽  
Liver Segmentation ◽  
Deep Convolutional Neural Networks ◽  
Adversarial Networks ◽  
Liver Region

Medical imaging provides a powerful tool for medical diagnosis. In the process of computer-aided diagnosis and treatment of liver cancer based on medical imaging, accurate segmentation of liver region from abdominal CT images is an important step. However, due to defects of liver tissue and limitations of CT imaging procession, the gray level of liver region in CT image is heterogeneous, and the boundary between the liver and those of adjacent tissues and organs is blurred, which makes the liver segmentation an extremely difficult task. In this study, aiming at solving the problem of low segmentation accuracy of the original 3D U-Net network, an improved network based on the three-dimensional (3D) U-Net, is proposed. Moreover, in order to solve the problem of insufficient training data caused by the difficulty of acquiring labeled 3D data, an improved 3D U-Net network is embedded into the framework of generative adversarial networks (GAN), which establishes a semi-supervised 3D liver segmentation optimization algorithm. Finally, considering the problem of poor quality of 3D abdominal fake images generated by utilizing random noise as input, deep convolutional neural networks (DCNN) based on feature restoration method is designed to generate more realistic fake images. By testing the proposed algorithm on the LiTS-2017 and KiTS19 dataset, experimental results show that the proposed semi-supervised 3D liver segmentation method can greatly improve the segmentation performance of liver, with a Dice score of 0.9424 outperforming other methods.

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