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Brain MRI Tumor Segmentation with Adversarial Networks [article]

Edoardo Giacomello, Daniele Loiacono, Luca Mainardi
2020 arXiv   pre-print
In this work, we introduce SegAN-CAT, an approach to brain tumor segmentation in Magnetic Resonance Images (MRI), based on Adversarial Networks.  ...  We tested our approach on two large datasets, made available by the Brain Tumor Image Segmentation Benchmark (BraTS).  ...  [3] proposed an Adversarial Network with a Multi-Scale loss, called SegAN, achieving better performances compared to the state-of-the-art methods for brain tumor segmentation [8] , [9] . B.  ... 
arXiv:1910.02717v2 fatcat:bf4zwamknbeijgp56rr6f4fj5e

Adversarial Perturbation on MRI Modalities in Brain Tumor Segmentation

Guohua Cheng, Hongli Ji
2020 IEEE Access  
tasks including lung segmentation [9] [10], brain tumor segmentation [11] , etc.  ...  The goal of brain tumor segmentation is to detect and localize tumor regions by comparing the tested brain tissue images to the normal brain tissue images [12] .  ... 
doi:10.1109/access.2020.3030235 fatcat:nycdixjkuvc7fg7yixi4cbsbfm

A Tetrahedron-Based Heat Flux Signature for Cortical Thickness Morphometry Analysis [chapter]

Yonghui Fan, Gang Wang, Natasha Lepore, Yalin Wang
2018 Lecture Notes in Computer Science  
Networks with Sample Selection by Relaxed Upper Confident Bound 660 Tumor-aware, Adversarial Domain Adaptation from CT to MRI for Lung Cancer Segmentation 667 A Novel Method for Epileptic Seizure Detection  ...  Autism Spectrum Disorder from Healthy Controls 571 Adaptive feature recombination and recalibration for semantic segmentation: application to brain tumor segmentation in MRI 572 Inherent Brain Segmentation  ... 
doi:10.1007/978-3-030-00931-1_48 pmid:30338317 pmcid:PMC6191198 fatcat:dqhvpm5xzrdqhglrfftig3qejq

Brain Tumour Segmentation Using U-net Based Adversarial Networks

Satyanarayana Teki, Mohan Varma, Anjana Yadav
2019 Traitement du signal  
This paper utilizes four state-of-the-art convolution architectures to perform the segmentation of brain tumour, including the generative adversarial networks (GANs), conditional deep convolution GANs,  ...  Based on adversarial networks, the author put forward a novel model for Image segmentation. The model consists of two parts: Auto encoders as generator and Convolution network as discriminator.  ...  of brain tumor.  ... 
doi:10.18280/ts.360408 fatcat:6jvvnqpconenli357bmqgzl4wa

Improvement of Multiparametric MR Image Segmentation by Augmenting the Data with Generative Adversarial Networks for Glioma Patients [article]

Eric Carver, Zhenzhen Dai, Evan Liang, James Snyder, Ning Wen
2019 arXiv   pre-print
created by a generative adversarial network to overcome the lack of annotated medical image data in training separate 2D U-Nets to segment enhancing tumor, peritumoral edema, and necrosis (non-enhancing  ...  Every year thousands of patients are diagnosed with a glioma, a type of malignant brain tumor. Physicians use MR images as a key tool in the diagnosis and treatment of these patients.  ...  Herein, we evaluate the performance of image segmentation using U-Net by augmenting the data with generative adversarial networks (GAN).  ... 
arXiv:1910.00696v1 fatcat:6pfwsrtczrfkrh4emkpadirchy

Brain Tumor Synthetic Segmentation in 3D Multimodal MRI Scans [article]

Mohammad Hamghalam, Baiying Lei, Tianfu Wang
2019 arXiv   pre-print
Specifically, generative adversarial network (GAN) is extended to synthesize high-contrast images.  ...  The magnetic resonance (MR) analysis of brain tumors is widely used for diagnosis and examination of tumor subregions.  ...  Synthesizing based on GAN network empower our model to decrease the number of real channels in multimodal brain tumor segmentation challenge 2019. Pred. LGG GT LGG Pred.  ... 
arXiv:1909.13640v1 fatcat:r6x5czkeafg2df2pbib7qsleai

Deep and Statistical Learning in Biomedical Imaging: State of the Art in 3D MRI Brain Tumor Segmentation [article]

K. Ruwani M. Fernando, Chris P. Tsokos
2021 arXiv   pre-print
In this study, we critically review major statistical and deep learning models and their applications in brain imaging research with a focus on MRI-based brain tumor segmentation.  ...  Clinical diagnostic and treatment decisions rely upon the integration of patient-specific data with clinical reasoning.  ...  Figure 5 : 5 Reconstruction of a healthy brain MRI using a VAE model with Multivariate Gaussian assumption. Figure 6 : 6 Adversarial training for tumor segmentation.  ... 
arXiv:2103.05529v1 fatcat:iqu5ix5tgre6pnokdmoejywh74

Conditional Adversarial Network for Semantic Segmentation of Brain Tumor [article]

Mina Rezaei, Konstantin Harmuth, Willi Gierke, Thomas Kellermeier, Martin Fischer, Haojin Yang, Christoph Meinel
2017 arXiv   pre-print
We exploit conditional Generative Adversarial Network (cGAN) and train a semantic segmentation Convolution Neural Network (CNN) along with an adversarial network that discriminates segmentation maps coming  ...  In this paper, we propose a novel end-to-end trainable architecture for brain tumor semantic segmentation through conditional adversarial training.  ...  Inspired by the power of cGAN networks [25, 9] , we propose an end-to-end trained adversarial deep structural network to perform brain High and Low Grade Glioma (HGG/LGG) tumor segmentation.  ... 
arXiv:1708.05227v1 fatcat:r4yk4p2tofdfrdtytsxwtrtxee

Medical Image Synthesis for Data Augmentation and Anonymization using Generative Adversarial Networks [article]

Hoo-Chang Shin, Neil A Tenenholtz, Jameson K Rogers, Christopher G Schwarz, Matthew L Senjem, Jeffrey L Gunter, Katherine Andriole, Mark Michalski
2018 arXiv   pre-print
In this work, we propose a method to generate synthetic abnormal MRI images with brain tumors by training a generative adversarial network using two publicly available data sets of brain MRI.  ...  First, we illustrate improved performance on tumor segmentation by leveraging the synthetic images as a form of data augmentation.  ...  Multi-parametric magnetic resonance images (MRIs) of abnormal brains (with tumor) are generated from segmentation masks of brain anatomy and tumor.  ... 
arXiv:1807.10225v2 fatcat:dcm3ta3mhbhr5pd3q2hdbljxam

ACN: Adversarial Co-training Network for Brain Tumor Segmentation with Missing Modalities [article]

Yixin Wang, Yang Zhang, Yang Liu, Zihao Lin, Jiang Tian, Cheng Zhong, Zhongchao Shi, Jianping Fan, Zhiqiang He
2021 arXiv   pre-print
Accurate segmentation of brain tumors from magnetic resonance imaging (MRI) is clinically relevant in diagnoses, prognoses and surgery treatment, which requires multiple modalities to provide complementary  ...  In this paper, we propose a novel Adversarial Co-training Network (ACN) to solve this issue, in which a series of independent yet related models are trained dedicated to each missing situation with significantly  ...  Conclusion In this work, we propose a novel Adversarial Co-training Network to address the problem of missing modalities in brain tumor segmentation.  ... 
arXiv:2106.14591v2 fatcat:t466n2lulzh4xfxk4o53p3yfsu

A Review of Deep-Learning-Based Medical Image Segmentation Methods

Xiangbin Liu, Liping Song, Shuai Liu, Yudong Zhang
2021 Sustainability  
With the rapid development of deep learning, medical image processing based on deep convolutional neural networks has become a research hotspot.  ...  Despite the great achievements of medical image segmentation in recent years, medical image segmentation based on deep learning has still encountered difficulties in research.  ...  Focusing on the segmentation task of MRI brain tumors, Giacomello et al. [74] proposed SegAN-CAT, a deep learning architecture based on a generative adversarial network.  ... 
doi:10.3390/su13031224 fatcat:pn2qbyv53zbuhhiuem2pc4dg3u

Deep Learning Based Brain Tumor Segmentation: A Survey [article]

Zhihua Liu, Lei Tong, Zheheng Jiang, Long Chen, Feixiang Zhou, Qianni Zhang, Xiangrong Zhang, Yaochu Jin, Huiyu Zhou
2021 arXiv   pre-print
Brain tumor segmentation is one of the most challenging problems in medical image analysis. The goal of brain tumor segmentation is to generate accurate delineation of brain tumor regions.  ...  A number of deep learning based methods have been applied to brain tumor segmentation and achieved promising results.  ...  Recently, researchers have applied generative adversarial networks (GANs) and auto-encoders (AEs) to brain tumor segmentation.  ... 
arXiv:2007.09479v3 fatcat:vdbpwfdsorfudkvnvottexd7je

Glioblastoma Multiforme Prognosis: MRI Missing Modality Generation, Segmentation and Radiogenomic Survival Prediction [article]

Mobarakol Islam, Navodini Wijethilake, Hongliang Ren
2021 arXiv   pre-print
Meanwhile, the same FCN architecture enables the tumor segmentation from the available and the synthesized MRI modalities.  ...  We exploit TCGA (The Cancer Genomic Atlas) dataset and synthesize the missing MRI modalities using a fully convolutional network (FCN) in a conditional Generative Adversarial Network (cGAN).  ...  Fig. 4 . 4 Our approaches for MRI translation and segmentation. (A) Proposed FCN uses as the generator network in cGAN; (B) Our model to segment the tumor from multi-modal MRI.  ... 
arXiv:2104.01149v2 fatcat:whst6y72qzfonfkeqkij6vrwmm

A Novel Domain Adaptation Framework for Medical Image Segmentation [article]

Amir Gholami and Shashank Subramanian and Varun Shenoy and Naveen Himthani and Xiangyu Yue and Sicheng Zhao and Peter Jin and George Biros and Kurt Keutzer
2018 arXiv   pre-print
Regarding our first innovation, we use a domain adaptation framework that combines a novel multispecies biophysical tumor growth model with a generative adversarial model to create realistic looking synthetic  ...  multimodal MR images with known segmentation.  ...  Then given an input 3D brain, we perform the following automatic steps to obtain the extended segmentation: Whole Brain Segmentation With Healthy Tissues 1.  ... 
arXiv:1810.05732v1 fatcat:3k2s34yh2nc6dgdql2lmrxal64

Brain Tumor Analysis Empowered with Deep Learning: A Review, Taxonomy, and Future Challenges

Muhammad Waqas Nadeem, Mohammed A. Al Ghamdi, Muzammil Hussain, Muhammad Adnan Khan, Khalid Masood Khan, Sultan H. Almotiri, Suhail Ashfaq Butt
2020 Brain Sciences  
Considering the wide range of applications of deep learning, the objective of this article is to review major deep learning concepts pertinent to brain tumor analysis (e.g., segmentation, classification  ...  A review conducted by summarizing a large number of scientific contributions to the field (i.e., deep learning in brain tumor analysis) is presented in this study.  ...  Fuzzy Logic with a Spiking Neuron Model (FL-SNM) is used for segmentation of the tumor region in MRIs.  ... 
doi:10.3390/brainsci10020118 pmid:32098333 pmcid:PMC7071415 fatcat:wofq4puvcbemlconbz6carsf2y
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