scholarly journals Freehand Ultrasound Image Simulation with Spatially-Conditioned Generative Adversarial Networks

2017 ◽  
pp. 105-115 ◽  
Author(s):  
Yipeng Hu ◽  
Eli Gibson ◽  
Li-Lin Lee ◽  
Weidi Xie ◽  
Dean C. Barratt ◽  
...  
Keyword(s):  
Ultrasound Image ◽  
Generative Adversarial Networks ◽  
Image Simulation ◽  
Adversarial Networks ◽  
Freehand Ultrasound

scholarly journals Breast Ultrasound Image Synthesis using Deep Convolutional Generative Adversarial Networks

Diagnostics ◽  
2019 ◽  
Vol 9 (4) ◽  
pp. 176 ◽  
Author(s):  
Tomoyuki Fujioka ◽  
Mio Mori ◽  
Kazunori Kubota ◽  
Yuka Kikuchi ◽  
Leona Katsuta ◽  
...  
Keyword(s):  
Ultrasound Image ◽  
Image Synthesis ◽  
Breast Ultrasound ◽  
Generative Adversarial Networks ◽  
Ultrasound Images ◽  
Clinical Value ◽  
Adversarial Networks ◽  
Significant Difference ◽  
Definition Of ◽  
The Masses

Deep convolutional generative adversarial networks (DCGANs) are newly developed tools for generating synthesized images. To determine the clinical utility of synthesized images, we generated breast ultrasound images and assessed their quality and clinical value. After retrospectively collecting 528 images of 144 benign masses and 529 images of 216 malignant masses in the breasts, synthesized images were generated using a DCGAN with 50, 100, 200, 500, and 1000 epochs. The synthesized (n = 20) and original (n = 40) images were evaluated by two radiologists, who scored them for overall quality, definition of anatomic structures, and visualization of the masses on a five-point scale. They also scored the possibility of images being original. Although there was no significant difference between the images synthesized with 1000 and 500 epochs, the latter were evaluated as being of higher quality than all other images. Moreover, 2.5%, 0%, 12.5%, 37.5%, and 22.5% of the images synthesized with 50, 100, 200, 500, and 1000 epochs, respectively, and 14% of the original images were indistinguishable from one another. Interobserver agreement was very good (|r| = 0.708–0.825, p < 0.001). Therefore, DCGAN can generate high-quality and realistic synthesized breast ultrasound images that are indistinguishable from the original images.

scholarly journals Cross Data Set Generalization of Ultrasound Image Augmentation using Representation Learning: A Case Study

2021 ◽  
Vol 7 (2) ◽  
pp. 755-758
Author(s):  
Daniel Wulff ◽  
Mohamad Mehdi ◽  
Floris Ernst ◽  
Jannis Hagenah
Keyword(s):  
Data Augmentation ◽  
Ultrasound Image ◽  
Image Data ◽  
Representation Learning ◽  
Generative Adversarial Networks ◽  
Data Sets ◽  
Imaging Data ◽  
Data Set ◽  
Adversarial Networks ◽  
Classical Image

Abstract Data augmentation is a common method to make deep learning assessible on limited data sets. However, classical image augmentation methods result in highly unrealistic images on ultrasound data. Another approach is to utilize learning-based augmentation methods, e.g. based on variational autoencoders or generative adversarial networks. However, a large amount of data is necessary to train these models, which is typically not available in scenarios where data augmentation is needed. One solution for this problem could be a transfer of augmentation models between different medical imaging data sets. In this work, we present a qualitative study of the cross data set generalization performance of different learning-based augmentation methods for ultrasound image data. We could show that knowledge transfer is possible in ultrasound image augmentation and that the augmentation partially results in semantically meaningful transfers of structures, e.g. vessels, across domains.

scholarly journals Empirical Analysis of Deep Convolutional Generative Adversarial Network for Ultrasound Image Synthesis

2021 ◽  
Vol 15 (1) ◽  
pp. 71-77
Author(s):  
Dheeraj Kumar ◽  
Mayuri A. Mehta ◽  
Indranath Chatterjee
Keyword(s):  
Empirical Analysis ◽  
Ultrasound Imaging ◽  
Ultrasound Image ◽  
Generative Adversarial Networks ◽  
Ultrasound Images ◽  
Generative Adversarial Network ◽  
Adversarial Network ◽  
Adversarial Networks ◽  
Synthetic Images

Introduction: Recent research on Generative Adversarial Networks (GANs) in the biomedical field has proven the effectiveness in generating synthetic images of different modalities. Ultrasound imaging is one of the primary imaging modalities for diagnosis in the medical domain. In this paper, we present an empirical analysis of the state-of-the-art Deep Convolutional Generative Adversarial Network (DCGAN) for generating synthetic ultrasound images. Aims: This work aims to explore the utilization of deep convolutional generative adversarial networks for the synthesis of ultrasound images and to leverage its capabilities. Background: Ultrasound imaging plays a vital role in healthcare for timely diagnosis and treatment. Increasing interest in automated medical image analysis for precise diagnosis has expanded the demand for a large number of ultrasound images. Generative adversarial networks have been proven beneficial for increasing the size of data by generating synthetic images. Objective: Our main purpose in generating synthetic ultrasound images is to produce a sufficient amount of ultrasound images with varying representations of a disease. Methods: DCGAN has been used to generate synthetic ultrasound images. It is trained on two ultrasound image datasets, namely, the common carotid artery dataset and nerve dataset, which are publicly available on Signal Processing Lab and Kaggle, respectively. Results: Results show that good quality synthetic ultrasound images are generated within 100 epochs of training of DCGAN. The quality of synthetic ultrasound images is evaluated using Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index Measure (SSIM). We have also presented some visual representations of the slices of generated images for qualitative comparison. Conclusion: Our empirical analysis reveals that synthetic ultrasound image generation using DCGAN is an efficient approach. Other: In future work, we plan to compare the quality of images generated through other adversarial methods such as conditional GAN, progressive GAN.

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