A Conditional Adversarial Network for Semantic Segmentation of Brain Tumor

Rezaei, Mina; Harmuth, Konstantin; Gierke, Willi; Kellermeier, Thomas; Fischer, Martin; Yang, Haojin; Meinel, Christoph

doi:10.1007/978-3-319-75238-9_21

Cited by 76 publications

(45 citation statements)

References 13 publications

(33 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…GANs for Medical Imagining and medical information processing [136], GANs for medical image de-noising with Wasserstein distance and perceptual loss [253]. GANs can also be used for segmentation of Brain Tumors with conditional GANs (cGAN) [254]. A General medical image segmentation approach is proposed using a GAN called SegAN [255].…”

Section: Gan For Medical Information Processingmentioning

confidence: 99%

A State-of-the-Art Survey on Deep Learning Theory and Architectures

et al. 2019

View full text Add to dashboard Cite

In recent years, deep learning has garnered tremendous success in a variety of application domains. This new field of machine learning has been growing rapidly and has been applied to most traditional application domains, as well as some new areas that present more opportunities. Different methods have been proposed based on different categories of learning, including supervised, semi-supervised, and un-supervised learning. Experimental results show state-of-the-art performance using deep learning when compared to traditional machine learning approaches in the fields of image processing, computer vision, speech recognition, machine translation, art, medical imaging, medical information processing, robotics and control, bioinformatics, natural language processing, cybersecurity, and many others. This survey presents a brief survey on the advances that have occurred in the area of Deep Learning (DL), starting with the Deep Neural Network (DNN). The survey goes on to cover Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), including Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), Auto-Encoder (AE), Deep Belief Network (DBN), Generative Adversarial Network (GAN), and Deep Reinforcement Learning (DRL). Additionally, we have discussed recent developments, such as advanced variant DL techniques based on these DL approaches. This work considers most of the papers published after 2012 from when the history of deep learning began. Furthermore, DL approaches that have been explored and evaluated in different application domains are also included in this survey. We also included recently developed frameworks, SDKs, and benchmark datasets that are used for implementing and evaluating deep learning approaches. There are some surveys that have been published on DL using neural networks and a survey on Reinforcement Learning (RL). However, those papers have not discussed individual advanced techniques for training large-scale deep learning models and the recently developed method of generative models.

show abstract

Section: Gan For Medical Information Processingmentioning

confidence: 99%

A State-of-the-Art Survey on Deep Learning Theory and Architectures

et al. 2019

View full text Add to dashboard Cite

show abstract

“…Mok and Chung proposed a new GAN architecture which utilizes a coarse-to-fine generator whose aim is to capture the manifold of the training data and generate augmented examples (Mok and Chung, 2018). Adversarial networks have been also used for semantic segmentation of brain tumors (Rezaei et al, 2017), brain-tumor detection (Varghese et al, 2017), and image synthesis of different modalities (Yu et al, 2018). Although GANs allow us to introduce invariance and robustness of deep models with respect to not only affine transforms (e.g., rotation, scaling, or flipping) but also to some shape and appearance variations, convergence of the adversarial training and existence of its equilibrium point remain the open issues.…”

Section: Data Augmentation By Generating Artificial Datamentioning

confidence: 99%

Data Augmentation for Brain-Tumor Segmentation: A Review

Nalepa

Marcinkiewicz²,

Kawulok

2019

Front. Comput. Neurosci.

245

127

View full text Add to dashboard Cite

Data augmentation is a popular technique which helps improve generalization capabilities of deep neural networks, and can be perceived as implicit regularization. It plays a pivotal role in scenarios in which the amount of high-quality ground-truth data is limited, and acquiring new examples is costly and time-consuming. This is a very common problem in medical image analysis, especially tumor delineation. In this paper, we review the current advances in data-augmentation techniques applied to magnetic resonance images of brain tumors. To better understand the practical aspects of such algorithms, we investigate the papers submitted to the Multimodal Brain Tumor Segmentation Challenge (BraTS 2018 edition), as the BraTS dataset became a standard benchmark for validating existent and emerging brain-tumor detection and segmentation techniques. We verify which data augmentation approaches were exploited and what was their impact on the abilities of underlying supervised learners. Finally, we highlight the most promising research directions to follow in order to synthesize high-quality artificial brain-tumor examples which can boost the generalization abilities of deep models.

show abstract

“…A similar approach was taken for structure correction in chest X-rays segmentation in [5]. A conditional GAN approach was taken in [10] for brain tumor segmentation.…”

Section: Introductionmentioning

confidence: 99%

PAN: Projective Adversarial Network for Medical Image Segmentation

Khosravan

Mortazi

Wallace

et al. 2019

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Adversarial learning has been proven to be effective for capturing long-range and high-level label consistencies in semantic segmentation. Unique to medical imaging, capturing 3D semantics in an effective yet computationally efficient way remains an open problem. In this study, we address this computational burden by proposing a novel projective adversarial network, called PAN, which incorporates high-level 3D information through 2D projections. Furthermore, we introduce an attention module into our framework that helps for a selective integration of global information directly from our segmentor to our adversarial network. For the clinical application we chose pancreas segmentation from CT scans. Our proposed framework achieved state-of-the-art performance without adding to the complexity of the segmentor.

show abstract

A Conditional Adversarial Network for Semantic Segmentation of Brain Tumor

Cited by 76 publications

References 13 publications

A State-of-the-Art Survey on Deep Learning Theory and Architectures

A State-of-the-Art Survey on Deep Learning Theory and Architectures

Data Augmentation for Brain-Tumor Segmentation: A Review

PAN: Projective Adversarial Network for Medical Image Segmentation

Contact Info

Product

Resources

About