scholarly journals TumorGAN: A Multi-Modal Data Augmentation Framework for Brain Tumor Segmentation

Sensors ◽  
2020 ◽  
Vol 20 (15) ◽  
pp. 4203 ◽  
Author(s):  
Qingyun Li ◽  
Zhibin Yu ◽  
Yubo Wang ◽  
Haiyong Zheng
Keyword(s):  
Brain Tumor ◽  
Data Augmentation ◽  
Labor Demand ◽  
Synthetic Data ◽  
Generative Adversarial Networks ◽  
Tumor Segmentation ◽  
Imaging Data ◽  
Brain Tumor Segmentation ◽  
Data Set ◽  
Image Pairs

The high human labor demand involved in collecting paired medical imaging data severely impedes the application of deep learning methods to medical image processing tasks such as tumor segmentation. The situation is further worsened when collecting multi-modal image pairs. However, this issue can be resolved through the help of generative adversarial networks, which can be used to generate realistic images. In this work, we propose a novel framework, named TumorGAN, to generate image segmentation pairs based on unpaired adversarial training. To improve the quality of the generated images, we introduce a regional perceptual loss to enhance the performance of the discriminator. We also develop a regional L1 loss to constrain the color of the imaged brain tissue. Finally, we verify the performance of TumorGAN on a public brain tumor data set, BraTS 2017. The experimental results demonstrate that the synthetic data pairs generated by our proposed method can practically improve tumor segmentation performance when applied to segmentation network training.

scholarly journals Improving Brain Tumor Segmentation with Data Augmentation Strategies

2021 ◽  
Author(s):  
Radhika Malhotra ◽  
Jasleen Saini ◽  
Barjinder Singh Saini ◽  
Savita Gupta
Keyword(s):  
Brain Tumor ◽  
Data Augmentation ◽  
Large Data ◽  
Training Data ◽  
Tumor Segmentation ◽  
Biomedical Image Processing ◽  
Brain Tumor Segmentation ◽  
Biomedical Image ◽  
Augmentation Strategies ◽  
Unseen Data

In the past decade, there has been a remarkable evolution of convolutional neural networks (CNN) for biomedical image processing. These improvements are inculcated in the basic deep learning-based models for computer-aided detection and prognosis of various ailments. But implementation of these CNN based networks is highly dependent on large data in case of supervised learning processes. This is needed to tackle overfitting issues which is a major concern in supervised techniques. Overfitting refers to the phenomenon when a network starts learning specific patterns of the input such that it fits well on the training data but leads to poor generalization abilities on unseen data. The accessibility of enormous quantity of data limits the field of medical domain research. This paper focuses on utility of data augmentation (DA) techniques, which is a well-recognized solution to the problem of limited data. The experiments were performed on the Brain Tumor Segmentation (BraTS) dataset which is available online. The results signify that different DA approaches have upgraded the accuracies for segmenting brain tumor boundaries using CNN based model.

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 Generative Adversarial Networks to Synthesize Missing T1 and FLAIR MRI Sequences for Use in a Multisequence Brain Tumor Segmentation Model

Radiology ◽  
2021 ◽  
Vol 300 (1) ◽  
pp. E319-E319
Author(s):  
Gian Marco Conte ◽  
Alexander D. Weston ◽  
David C. Vogelsang ◽  
Kenneth A. Philbrick ◽  
Jason C. Cai ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Generative Adversarial Networks ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Adversarial Networks ◽  
Mri Sequences

Generative Adversarial Networks to Synthesize Missing T1 and FLAIR MRI Sequences for Use in a Multisequence Brain Tumor Segmentation Model

Radiology ◽  
2021 ◽  
Vol 299 (2) ◽  
pp. 313-323 ◽  
Author(s):  
Gian Marco Conte ◽  
Alexander D. Weston ◽  
David C. Vogelsang ◽  
Kenneth A. Philbrick ◽  
Jason C. Cai ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Generative Adversarial Networks ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation ◽  
Adversarial Networks ◽  
Mri Sequences

Data Augmentation for Improved Brain Tumor Segmentation

2021 ◽  
pp. 1-11
Author(s):  
Ankur Biswas ◽  
Paritosh Bhattacharya ◽  
Santi P. Maity ◽  
Rita Banik
Keyword(s):  
Brain Tumor ◽  
Data Augmentation ◽  
Tumor Segmentation ◽  
Brain Tumor Segmentation

scholarly journals Bidirectional ConvLSTMXNet for Brain Tumor Segmentation of MR Images

2021 ◽  
Vol 15 (1) ◽  
pp. 37-42
Author(s):  
M. Ravikumar ◽  
B.J. Shivaprasad
Keyword(s):  
Deep Learning ◽  
Brain Tumor ◽  
Tumor Segmentation ◽  
Mr Images ◽  
Mr Image ◽  
Brain Tumor Segmentation ◽  
Data Set ◽  
Tumour Segmentation ◽  
Brain Tumour Segmentation

In recent years, deep learning based networks have achieved good performance in brain tumour segmentation of MR Image. Among the existing networks, U-Net has been successfully applied. In this paper, it is propose deep-learning based Bidirectional Convolutional LSTM XNet (BConvLSTMXNet) for segmentation of brain tumor and using GoogLeNet classify tumor & non-tumor. Evaluated on BRATS-2019 data-set and the results are obtained for classification of tumor and non-tumor with Accuracy: 0.91, Precision: 0.95, Recall: 1.00 & F1-Score: 0.92. Similarly for segmentation of brain tumor obtained Accuracy: 0.99, Specificity: 0.98, Sensitivity: 0.91, Precision: 0.91 & F1-Score: 0.88.

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