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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
Brain tumors segmentation has a special importance and difficulty due to the difference in appearances and shapes of the different tumor regions in magnetic resonance images.  ...  We also propose an end-to-end trainable CNN for survival day prediction based on deep learning techniques for BraTS 2017 prediction task [15, 4, 2, 3].  ...  -We proposed an automatic and trainable deep learning architecture for survival day prediction based on clinical data and MR images.  ... 
arXiv:1708.05227v1 fatcat:r4yk4p2tofdfrdtytsxwtrtxee

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  
Deep Neural Network and Statistical Shape Model for Pancreas Segmentation 327 One-pass Multi-task Convolutional Neural Networks for Efficient Brain Tumor Segmentation 329 MRI Measurement of Placental  ...  Segmentation 249 A Feature-Driven Active Framework for Ultrasound-Based Brain Shift Compensation 264 Esophageal Gross Tumor Volume Segmentation using a 3D Convolutional Neural Network 274 Cardiac MR Segmentation  ... 
doi:10.1007/978-3-030-00931-1_48 pmid:30338317 pmcid:PMC6191198 fatcat:dqhvpm5xzrdqhglrfftig3qejq

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  ...  Consequently, deep learning has dramatically changed and improved the means of recognition, prediction, and diagnosis effectively in numerous areas of healthcare such as pathology, brain tumor, lung cancer  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/brainsci10020118 pmid:32098333 pmcid:PMC7071415 fatcat:wofq4puvcbemlconbz6carsf2y

Overview of Multi-Modal Brain Tumor MR Image Segmentation

Wenyin Zhang, Yong Wu, Bo Yang, Shunbo Hu, Liang Wu, Sahraoui Dhelimd
2021 Healthcare  
methods, and segmentation methods based on deep learning methods.  ...  The precise segmentation of brain tumor images is a vital step towards accurate diagnosis and effective treatment of brain tumors.  ...  Segmentation Methods of Brain Tumor MR Images Based on Deep Learning According to different network frameworks, the brain MR image segmentation method is based on deep learning and can be divided into  ... 
doi:10.3390/healthcare9081051 fatcat:hnx3rjoo6bdzzefk7k4qntxmje

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.  ...  Driven by the breakthroughs in computer vision, deep learning became the de facto standard in the domain of medical imaging.  ...  In order to utilize contextual information in brain volumetry on MR images, 3D deep networks are typically deployed, which uses extensive memory.  ... 
arXiv:2103.05529v1 fatcat:iqu5ix5tgre6pnokdmoejywh74

Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions

Zeynettin Akkus, Alfiia Galimzianova, Assaf Hoogi, Daniel L. Rubin, Bradley J. Erickson
2017 Journal of digital imaging  
First we review the current deep learning architectures used for segmentation of anatomical brain structures and brain lesions.  ...  Quantitative analysis of brain MRI is routine for many neurological diseases and conditions and relies on accurate segmentation of structures of interest.  ...  Acknowledgements This work was supported by National Institutes of  ... 
doi:10.1007/s10278-017-9983-4 pmid:28577131 pmcid:PMC5537095 fatcat:lekbdtmkx5cchmuutntacymrzu

Automated brain tumor segmentation on multi-modal MR image using SegNet

Salma Alqazzaz, Xianfang Sun, Xin Yang, Len Nokes
2019 Computational Visual Media  
In this paper, we tackle this problem by applying a fully convolutional neural network SegNet to 3D data sets for four MRI modalities (Flair, T1, T1ce, and T2) for automated segmentation of brain tumor  ...  Manual segmentation of brain tumor regions is not only timeconsuming but also prone to human error, and its performance depends on pathologists' experience.  ...  Acknowledgements We would like to thank nVidia for their kind donation of a Titan XP GPU.  ... 
doi:10.1007/s41095-019-0139-y fatcat:ade5gnwqxbczvhvw7sjszc6ybi

Brain Image Segmentation in Recent Years: A Narrative Review

Ali Fawzi, Anusha Achuthan, Bahari Belaton
2021 Brain Sciences  
From this review, it is found that deep learning-based and hybrid-based metaheuristic approaches are more efficient for the reliable segmentation of brain tumors.  ...  Brain image segmentation is one of the most time-consuming and challenging procedures in a clinical environment. Recently, a drastic increase in the number of brain disorders has been noted.  ...  Acknowledgments: The authors would thank the Ministry of Higher Education Malaysia And Universiti Sains Malaysia for providing the infrastructures and supports to complete this work.  ... 
doi:10.3390/brainsci11081055 fatcat:cdie3nuxzzfevoynik3iqtenli

Emergence of Convolutional Neural Network in Future Medicine: Why and How. A Review on Brain Tumor Segmentation

Behrouz Alizadeh Savareh, Hassan Emami, Mohamadreza Hajiabadi, Mahyar Ghafoori, Seyed Majid Azimi
2018 Polish Journal of Medical Physics And Engineering  
Manual analysis of brain tumors magnetic resonance images is usually accompanied by some problem. Several techniques have been proposed for the brain tumor segmentation.  ...  This study will be focused on searching popular databases for related studies, theoretical and practical aspects of Convolutional Neural Network surveyed in brain tumor segmentation.  ...  Figure 1 shows some examples of brain tumor MR images with their ground truth segmentation (manually segmented by experts).  ... 
doi:10.2478/pjmpe-2018-0007 fatcat:2z6vsmuhwrgj7fcmhmidurrp5u

Recent Advancements in Brain Tumor Segmentation and Classification using Deep Learning: A Review

Muhmmad Irfan Sharif, Srinivas Institute of Technology
2019 International Journal of Engineering Research and  
The review aims to provide an introduction to recent works that use deep learning methodologies for brain tumor analysis.  ...  Nowadays, with the advent of technology, it is becoming desirable to perform automated computer-based brain tumor image analysis.  ...  CONCLUSION A lot of work in performed in recent days on brain tumor MRI image segmentation and prediction with deep approaches.  ... 
doi:10.17577/ijertv8is120190 fatcat:fma2gzxzmrb5daxiz2zumujgqq

A Review Article on Brain Tumor Detection and Optimization using Hybrid Classification Algorithm

Nitesh Yadav
2021 International Journal for Research in Applied Science and Engineering Technology  
In most applications, machine learning shows better performance than manual segmentation of the brain tumors from MRI images as it is a difficult and timeconsuming task.  ...  Abstract: This review focuses on different imaging techniques such as MRI. This survey identifies a different approach with better accuracy for tumor detection.  ...  LITERATURE REVIEW MAISHA FARZANA, Semantic Segmentation of Brain Tumor from 3D Structural MRI Using U-Net Autoencoder: Automated semantic segmentation of brain tumors from 3D MRI images plays a significant  ... 
doi:10.22214/ijraset.2021.38903 fatcat:hjeg36lrcjeolke3ryukofhjny

Multi-Task Deep Supervision on Attention R2U-Net for Brain Tumor Segmentation

Shiqiang Ma, Jijun Tang, Fei Guo
2021 Frontiers in Oncology  
MTDS can extract rich semantic information from images, obtain accurate segmentation boundaries, and prevent overfitting problems in deep learning.  ...  Accurate automatic medical image segmentation technology plays an important role for the diagnosis and treatment of brain tumor.  ...  This strategy can enable the deep learning network to learn the shape characteristics of brain tumors, and use the shape information of brain tumors and nontumor regions to help the network distinguish  ... 
doi:10.3389/fonc.2021.704850 pmid:34604039 pmcid:PMC8484917 fatcat:ajjjhte4tfes7clnwxv3zstd4u

Brain Tumor Segmentation and Survival Prediction [article]

Rupal Agravat, Mehul S Raval
2019 arXiv   pre-print
The paper demonstrates the use of the fully convolutional neural network for glioma segmentation on the BraTS 2019 dataset.  ...  Radiomic features along with segmentation results and age are used to predict the overall survival of patients using random forest regressor to classify survival of patients in long, medium and short survival  ...  Acknowledgement The authors would like to thank NVIDIA Corporation for donating the Quadro K5200 and Quadro P5000 GPU used for this research, Dr.  ... 
arXiv:1909.09399v1 fatcat:ylt6dyankzf7lkkqyuvde77rca

Development of brain tumor segmentation of magnetic resonance imaging (MRI) using U-Net deep learning

Wasan M. Jwaid, Zainab Shaker Matar Al-Husseini, Ahmad H. Sabry
2021 Eastern-European Journal of Enterprise Technologies  
This paper proposes a robust U-Net deep learning Convolutional Neural Network (CNN) model that can classify if the subject has a tumor or not based on Brain Magnetic resonance imaging (MRI) with acceptable  ...  The results using Matlab-based toolbox indicate that the proposed architecture has been successfully evaluated and experienced for MRI datasets of brain tumor segmentation including 336 images as training  ...  SEGMENTATION OF MAGNETIC RESONANCE IMAGING (MRI) USING U-NET DEEP LEARNING deep learning CNN to classify the input brain images into a tumor or not based on the MRI dataset.  ... 
doi:10.15587/1729-4061.2021.238957 fatcat:widnihwzobflhkrgha7mlat7fq

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.  ...  Considering the remarkable breakthroughs made by state-of-the-art technologies, we use this survey to provide a comprehensive study of recently developed deep learning based brain tumor segmentation techniques  ...  Contribution of this survey With the breakthrough improvement made by deep learning in recent years, numerous deep learning based methods have been published on brain tumor segmentation and achieved promising  ... 
arXiv:2007.09479v3 fatcat:vdbpwfdsorfudkvnvottexd7je
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