Corpus ID: 221655410

Synthesizing brain tumor images and annotations by combining progressive growing GAN and SPADE

@article{Foroozandeh2020SynthesizingBT,
  title={Synthesizing brain tumor images and annotations by combining progressive growing GAN and SPADE},
  author={Mehdi Foroozandeh and Anders Eklund},
  journal={ArXiv},
  year={2020},
  volume={abs/2009.05946}
}
Training segmentation networks requires large annotated datasets, but manual annotation is time consuming and costly. We here investigate if the combination of a noise-to-image GAN and an image-to-image GAN can be used to synthesize realistic brain tumor images as well as the corresponding tumor annotations (labels), to substantially increase the number of training images. The noise-to-image GAN is used to synthesize new label images, while the image-to-image GAN generates the corresponding MR… Expand

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References

SHOWING 1-10 OF 28 REFERENCES
GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification
TLDR
It is shown that generated medical images can be used for synthetic data augmentation, and improve the performance of CNN for medical image classification, and generalize to other medical classification applications and thus support radiologists’ efforts to improve diagnosis. Expand
Feeding the zombies: Synthesizing brain volumes using a 3D progressive growing GAN
  • A. Eklund
  • Computer Science, Engineering
  • ArXiv
  • 2019
TLDR
Preliminary results are presented showing that a 3D progressive growing GAN can be used to synthesize MR brain volumes. Expand
U-Net: Convolutional Networks for Biomedical Image Segmentation
TLDR
It is shown that such a network can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Expand
The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)
TLDR
The set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences are reported, finding that different algorithms worked best for different sub-regions, but that no single algorithm ranked in the top for all sub-Regions simultaneously. Expand
A survey on deep learning in medical image analysis
TLDR
This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year, to survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks. Expand
GAN Augmentation: Augmenting Training Data using Generative Adversarial Networks
TLDR
The feasibility of introducing GAN derived synthetic data to the training datasets in two brain segmentation tasks is demonstrated, leading to improvements in Dice Similarity Coefficient of between 1 and 5 percentage points under different conditions, with the strongest effects seen fewer than ten training image stacks are available. Expand
Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features
TLDR
This set of labels and features should enable direct utilization of the TCGA/TCIA glioma collections towards repeatable, reproducible and comparative quantitative studies leading to new predictive, prognostic, and diagnostic assessments, as well as performance evaluation of computer-aided segmentation methods. Expand
Progressive Growing of GANs for Improved Quality, Stability, and Variation
TLDR
A new training methodology for generative adversarial networks is described, starting from a low resolution, and adding new layers that model increasingly fine details as training progresses, allowing for images of unprecedented quality. Expand
Synthetic Medical Images from Dual Generative Adversarial Networks
TLDR
A novel, two-stage pipeline for generating synthetic medical images from a pair of generative adversarial networks, tested in practice on retinal fundi images and develops a hierarchical generation process to divide the complex image generation task into two parts: geometry and photorealism. Expand
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
TLDR
This study assesses the state-of-the-art machine learning methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018, and investigates the challenge of identifying the best ML algorithms for each of these tasks. Expand
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