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Automatic Segmentation of MR Brain Images With a Convolutional Neural Network

Pim Moeskops, Max A. Viergever, Adrienne M. Mendrik, Linda S. de Vries, Manon J. N. L. Benders, Ivana Isgum
2016 IEEE Transactions on Medical Imaging  
This paper presents a method for the automatic segmentation of MR brain images into a number of tissue classes using a convolutional neural network.  ...  Automatic segmentation in MR brain images is important for quantitative analysis in large-scale studies with images acquired at all ages.  ...  ACKNOWLEDGMENT The authors gratefully acknowledge the support of NVIDIA Corporation with the donation of the Tesla K40 GPU used in this research.  ... 
doi:10.1109/tmi.2016.2548501 pmid:27046893 fatcat:7kcpxd3jgzb7hixo6swanw2tsa

Front Matter: Volume 10574

Proceedings of SPIE, Elsa D. Angelini, Bennett A. Landman
2018 Medical Imaging 2018: Image Processing  
These two-number sets start with 00, 01, 02, 03,  ...  A unique citation identifier (CID) number is assigned to each article at the time of publication.  ...  and conditional generative adversarial networks 10574 0A Segmentation of left ventricle myocardium in porcine cardiac cine MR images using a hybrid of fully convolutional neural networks and convolutional  ... 
doi:10.1117/12.2315755 fatcat:jdfbaent6vhu5dwlrqrqt66vce

Brain Tumor Segmentation Using Deep Learning by Type Specific Sorting of Images [article]

Zahra Sobhaninia, Safiyeh Rezaei, Alireza Noroozi, Mehdi Ahmadi, Hamidreza Zarrabi, Nader Karimi, Ali Emami, Shadrokh Samavi
2018 arXiv   pre-print
The effect of using separate networks for segmentation of MR images is evaluated by comparing the results with a single network.  ...  Here we present a solution for brain tumor segmenting by using deep learning. In this work, we studied different angles of brain MR images and applied different networks for segmentation.  ...  In this work, we present an automatic brain tumor segmentation technique based on Convolutional Neural Network. We have used three MRI views of human brain.  ... 
arXiv:1809.07786v1 fatcat:6elpt7iz4rhxtijlkjo6odnume

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  
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  ...  Convolutional Neural Networks 567 Deep learning with synthetic diffusion MRI data for free-water elimination in glioblastoma cases 568 3D Deep Convolutional Neural Network Revealed the Value of Brain  ... 
doi:10.1007/978-3-030-00931-1_48 pmid:30338317 pmcid:PMC6191198 fatcat:dqhvpm5xzrdqhglrfftig3qejq

Overview of Multi-Modal Brain Tumor MR Image Segmentation

Wenyin Zhang, Yong Wu, Bo Yang, Shunbo Hu, Liang Wu, Sahraoui Dhelimd
2021 Healthcare  
The precise segmentation of brain tumor images is a vital step towards accurate diagnosis and effective treatment of brain tumors.  ...  In this paper, we survey the field of brain tumor MRI images segmentation. Firstly, we present the commonly used databases.  ...  that based on Convolutional Neural network (Convolutional Neural Networks, CNN) of the brain MR image segmentation method, and that based on the Convolutional Neural network (Fully Convolutional Networks  ... 
doi:10.3390/healthcare9081051 fatcat:hnx3rjoo6bdzzefk7k4qntxmje

Front Matter: Volume 10134

2017 Medical Imaging 2017: Computer-Aided Diagnosis  
These two-number sets start with 00, 01, 02, 03, 04,  ...  A unique citation identifier (CID) number is assigned to each article at the time of publication.  ...  09 3D convolutional neural network for automatic detection of lung nodules in chest CT 10134 0A Automatic detection of lung nodules: false positive reduction using convolution neural networks and handcrafted  ... 
doi:10.1117/12.2277119 dblp:conf/micad/X17 fatcat:ika7pheqxngdxejyvkss4dkbv4

Automatic Brain Tumor Segmentation Using Cascaded Anisotropic Convolutional Neural Networks [chapter]

Guotai Wang, Wenqi Li, Sébastien Ourselin, Tom Vercauteren
2018 Lecture Notes in Computer Science  
A cascade of fully convolutional neural networks is proposed to segment multi-modal Magnetic Resonance (MR) images with brain tumor into background and three hierarchical regions: whole tumor, tumor core  ...  Our networks consist of multiple layers of anisotropic and dilated convolution filters, and they are combined with multi-view fusion to reduce false positives.  ...  Conclusion We developed a cascaded system to segment glioma subregions from multimodality brain MR images.  ... 
doi:10.1007/978-3-319-75238-9_16 fatcat:6hy5glbioja3pkvajjueulpbfy

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.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/brainsci10020118 pmid:32098333 pmcid:PMC7071415 fatcat:wofq4puvcbemlconbz6carsf2y

Front Matter: Volume 10575

Proceedings of SPIE, Kensaku Mori, Nicholas Petrick
2018 Medical Imaging 2018: Computer-Aided Diagnosis  
These two-number sets start with 00, 01, 02, 03,  ...  SPIEDigitalLibrary.org Paper Numbering: Proceedings of SPIE follow an e-First publication model. A unique citation identifier (CID) number is assigned to each article at the time of publication.  ...  1R Detection of brain tumor margins using optical coherence tomography [10575-62] 10575 1S Classification of brain MRI with big data and deep 3D convolutional neural networks [10575-63] 10575 1T  ... 
doi:10.1117/12.2315758 fatcat:kqpt2ugrxrgx7m5rhasawarque

Front Matter: Volume 10133

Proceedings of SPIE, Martin A. Styner, Elsa D. Angelini
2017 Medical Imaging 2017: Image Processing  
using deep convolutional neural networks [10133-28] 10133 0T Automatic localization of cochlear implant electrodes in CTs with a limited intensity range [10133-29] 10133 0U Fully automated lobe-based  ...  image segmentation [10133-90] 10133 2H Automatic MR prostate segmentation by deep learning with holistically-nested networks [10133-91] 10133 2I Fully automated lumen segmentation of intracoronary  ...  SESSION 4 SEGMENTATION: BRAIN Session Chairs  ... 
doi:10.1117/12.2270368 dblp:conf/miip/X17 fatcat:resfpzholvbtbalfkbg3pj64gu

Fast and robust segmentation of the striatum using deep convolutional neural networks

Hongyoon Choi, Kyong Hwan Jin
2016 Journal of Neuroscience Methods  
h i g h l i g h t s • We describe a new method for stratium segmentation. • We employ two serial deep convolutional neural networks (CNN). • Segmentation accuracy of deep CNN is comparable with that of  ...  We developed a fast and accurate method for the striatum segmentation using deep convolutional neural networks (CNN).  ...  We aimed to develop a rapid and robust automated segmentation method for the striatum using deep convolutional neural network (CNN).  ... 
doi:10.1016/j.jneumeth.2016.10.007 pmid:27777000 fatcat:z4jjkpynhvbldjrsj6dzsbr6fm

Front Matter: Volume 12032

Ivana Išgum, Olivier Colliot
2022 Medical Imaging 2022: Image Processing  
These two-number sets start with 00,  ...  of SPIE at the time of publication.  ...  network and one-shot learning [12032-14] 0H Evaluating the impact of MR image harmonization on thalamus deep network segmentation [12032-15] 0I Evaluating the impact of MR image contrast on whole brain  ... 
doi:10.1117/12.2638192 fatcat:ikfgnjefaba2tpiamxoftyi6sa

Automatic segmentation of the intracranialvolume in fetal MR images [article]

N. Khalili, P. Moeskops, N.H.P. Claessens, S. Scherpenzeel, E. Turk, R. de Heus, M.J.N.L. Benders, M.A. Viergever, J.P.W. Pluim, I. Išgum
2017 arXiv   pre-print
This paper presents an automatic method for segmentation of the ICV in fetal MR images.  ...  The method employs a multi-scale convolutional neural network in 2D slices to enable learning spatial information from larger context as well as detailed local information.  ...  Acknowledgements This study was sponsored by the Research Program Specialized Nutrition of the Utrecht Center for Food and Health, through a subsidy from the Dutch Ministry of Economic Affairs, the Utrecht  ... 
arXiv:1708.02282v1 fatcat:ltqt5u7fg5drrfaec567q3yxse

Brain Abnormality Detection by Deep Convolutional Neural Network [article]

Mina Rezaei, Haojin Yang, Christoph Meinel
2017 arXiv   pre-print
We have achieved a promising result in five categories of brain images (classification task) with 95.7% accuracy.  ...  In this paper, we describe our method for classification of brain magnetic resonance (MR) images into different abnormalities and healthy classes based on the deep neural network.  ...  Figure 2 .Figure 4 .Figure 1 .Figure 3 : 2413 Our convolution neural network consists of several convolutional layers, max and average pooling layers, fully-connected layers, a dropout layer, and a final  ... 
arXiv:1708.05206v1 fatcat:npbhlshy6ze3nixv66sekmqk7u

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  
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.  ...  Manual analysis of brain tumors magnetic resonance images is usually accompanied by some problem. Several techniques have been proposed for the brain tumor segmentation.  ...  "brain MRI segmentation" "deep learning" and "convolutional neural network".  ... 
doi:10.2478/pjmpe-2018-0007 fatcat:2z6vsmuhwrgj7fcmhmidurrp5u
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