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Semi-automated brain tumor segmentation on multi-parametric MRI using regularized non-negative matrix factorization

Nicolas Sauwen, Marjan Acou, Diana M. Sima, Jelle Veraart, Frederik Maes, Uwe Himmelreich, Eric Achten, Sabine Van Huffel
2017 BMC Medical Imaging  
Methods: We present a semi-automated framework for brain tumor segmentation based on non-negative matrix factorization (NMF) that does not require prior training of the method.  ...  Results: Using L1-regularized semi-automated NMF segmentation, mean Dice-scores of 65%, 74 and 80% are found for active tumor, the tumor core and the whole tumor region.  ...  In the current study, we propose a semi-automated brain tumor segmentation method based on regularized non-negative matrix factorization.  ... 
doi:10.1186/s12880-017-0198-4 pmid:28472943 pmcid:PMC5418702 fatcat:jpq37evzfzc7hhoyjfqzelg3wi

Out-of-atlas labeling: A multi-atlas approach to cancer segmentation

Andrew J. Asman, Bennett A. Landman
2012 2012 9th IEEE International Symposium on Biomedical Imaging (ISBI)  
We propose a novel out-of-atlas technique to estimate the spatial extent of abnormal brain regions by combining multi-atlas based segmentation with semilocal non-parametric intensity analysis.  ...  Conventional automated segmentation techniques for magnetic resonance imaging (MRI) fail to perform in a robust and consistent manner when brain anatomy differs wildly from expectationsas is often the  ...  THEORY To construct the out-of-atlas estimate, a non-parametric estimate of the expected semi-local intensity distribution is constructed using the registered atlases and their associated labels and compared  ... 
doi:10.1109/isbi.2012.6235785 pmid:24443679 pmcid:PMC3892947 dblp:conf/isbi/AsmanL12 fatcat:wepi35cfnjewhi5m52lugkfewq

Front Matter: Volume 11313

Bennett A. Landman, Ivana Išgum
2020 Medical Imaging 2020: Image Processing  
using a Base 36 numbering system employing both numerals and letters.  ...  The papers in this volume were part of the technical conference cited on the cover and title page. Papers were selected and subject to review by the editors and conference program committee.  ...  for assessing tumor metabolic response [11313-62] POSTER SESSION 11313 1S Identifying the common and subject-specific functional units of speech movements via a joint sparse non-negative matrix  ... 
doi:10.1117/12.2570657 fatcat:be32besqknaybh6wibz7unuboa

Comparison of unsupervised classification methods for brain tumor segmentation using multi-parametric MRI

N. Sauwen, M. Acou, S. Van Cauter, D.M. Sima, J. Veraart, F. Maes, U. Himmelreich, E. Achten, S. Van Huffel
2016 NeuroImage: Clinical  
We demonstrate that a hierarchical non-negative matrix factorization variant which was previously introduced for MP-MRI tumor segmentation gives the best performance in terms of mean Dicescores for the  ...  characterization, hence there have been recent suggestions of combining different MRI modalities into a multi-parametric MRI (MP-MRI) approach for brain tumor segmentation.  ...  This paper reflects only the authors' views and the Union is not liable for any use that may be made of the contained information.  ... 
doi:10.1016/j.nicl.2016.09.021 pmid:27812502 pmcid:PMC5079350 fatcat:vnicyvw34bgtjc6yb3eegn4gzi

Taxonomy Of Brain Tumor Classification Techniques: A Systematic Review

Virupakshappa, Dr. Basavraj Amarapur
2017 Zenodo  
This survey tries to classify brain MRI images into normal, benign and malignant tumor.  ...  The image processing techniques consist of image pre-processing, enhancement, segmentation, feature extraction and classification implemented for the detection and classification of tumor from MRI images  ...  light of multi-parametric MR images.  ... 
doi:10.5281/zenodo.996584 fatcat:u5xf4qxodrgg3ezlmajxie5v2e

Taxonomy Of Brain Tumor Classification Techniques: A Systematic Review

Virupakshappa, Dr. Basavaraj Amarapur
2017 Zenodo  
This survey serves to classify the brain MRI images into normal, benign and malignant tumor.  ...  The image processing techniques implemented for the detection of tumor from MRI images consist of image pre-processing, segmentation, feature extraction and classification steps.  ...  light of multi-parametric MR images.  ... 
doi:10.5281/zenodo.1013807 fatcat:srcgtw7mmzdzzhwrnn3w4saigu

Fully Automated Enhanced Tumor Compartmentalization: Man vs. Machine Reloaded

Nicole Porz, Simon Habegger, Raphael Meier, Rajeev Verma, Astrid Jilch, Jens Fichtner, Urspeter Knecht, Christian Radina, Philippe Schucht, Jürgen Beck, Andreas Raabe, Johannes Slotboom (+3 others)
2016 PLoS ONE  
Objective Comparison of a fully-automated segmentation method that uses compartmental volume information to a semi-automatic user-guided and FDA-approved segmentation technique.  ...  Fully Automated Tumor Compartmentalization: Man vs. Machine Reloaded PLOS ONE | Fig 2.  ...  Fig 1 . 1 Set of MRI sequences used in this study for manual, automatic, and semi-automatic tumor volumetry.  ... 
doi:10.1371/journal.pone.0165302 pmid:27806121 pmcid:PMC5091868 fatcat:pxkak2tjnvfgnmjmpqkke2qp6y

Prediction of Glioma Grades Using Deep Learning with Wavelet Radiomic Features

Gökalp Çinarer, Bülent Gürsel Emiroğlu, Ahmet Haşim Yurttakal
2020 Applied Sciences  
This study primarily focuses on the four main aspects of the radiomic workflow, namely tumor segmentation, feature extraction, analysis, and classification.  ...  The purpose of this study is to develop a deep learning-based classification method using radiomic features of brain tumor glioma grades with deep neural network (DNN).  ...  Rundo et al. proposed a fully automated multimodal segmentation approach to separate Biological Target Volume (BTV) and Gross Target Volume (GTV) from PET and MRI images using 19 metastatic brain tumors  ... 
doi:10.3390/app10186296 fatcat:7nvzwovpkbdpbeo4duzk4u3yha

Deep Learning for Brain Tumor Segmentation: A Survey of State-of-the-Art

Tirivangani Magadza, Serestina Viriri
2021 Journal of Imaging  
The accurate segmentation of lesions requires more than one image modalities with varying contrasts.  ...  This paper presents a review of state-of-the-art deep learning methods for brain tumor segmentation, clearly highlighting their building blocks and various strategies.  ...  Semi-Automatic Segmentation Semi-automated segmentation combines both computer and human expertise.  ... 
doi:10.3390/jimaging7020019 pmid:34460618 fatcat:54xxzhauifdfbf2dwkqr3bfyei

Segmentation of Brain Tumors in MRI Images Using Three-Dimensional Active Contour without Edge

Ali Hasan, Farid Meziane, Rob Aspin, Hamid Jalab
2016 Symmetry  
Hence, this study proposes an automated method that can identify tumor slices and segment the tumor across all image slices in volumetric MRI brain scans.  ...  A contrast material is commonly used to enhance the tumor boundary against the surrounding normal brain tissue on T1-w images.  ...  Acknowledgments: The authors would like to thank the MRI Unit of Al Kadhimiya Teaching Hospital in Baghdad, Iraq for providing us with MRI brain scanning images dataset.  ... 
doi:10.3390/sym8110132 fatcat:yhfrq27wevanlkkymsvesxavzi

The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)

Bjoern H. Menze, Andras Jakab, Stefan Bauer, Jayashree Kalpathy-Cramer, Keyvan Farahani, Justin Kirby, Yuliya Burren, Nicole Porz, Johannes Slotboom, Roland Wiest, Levente Lanczi, Elizabeth Gerstner (+56 others)
2015 IEEE Transactions on Medical Imaging  
Algorithm and Data: In this work, we propose fully automated multi-class abnormal brain tissue segmentation in multimodality brain MRI.  ...  that was developed for fully automated MRI brain segmentation.  ... 
doi:10.1109/tmi.2014.2377694 pmid:25494501 pmcid:PMC4833122 fatcat:csrnfqc4i5eilh7wk5howvpr4u

MBIS: Multivariate Bayesian Image Segmentation tool

Oscar Esteban, Gert Wollny, Subrahmanyam Gorthi, María-J. Ledesma-Carbayo, Jean-Philippe Thiran, Andrés Santos, Meritxell Bach-Cuadra
2014 Computer Methods and Programs in Biomedicine  
MBIS supports multi-channel bias field correction based on a B-spline model.  ...  Along with MBIS, we release an evaluation framework that contains three different experiments on multi-site data.  ...  The accurate and automated segmentation of tumor and edema in multivariate brain images is an active field of interest in medical image analysis, as illustrated by the Challenge on Multimodal Brain Tumor  ... 
doi:10.1016/j.cmpb.2014.03.003 pmid:24768617 fatcat:czqd5w2advat7gyln6uiw7atzq

A Review on Automated Brain Tumor Detection and Segmentation from MRI of Brain [article]

Sudipta Roy, Sanjay Nag, Indra Kanta Maitra, Samir Kumar Bandyopadhyay
2013 arXiv   pre-print
There are different brain tumor detection and segmentation methods to detect and segment a brain tumor from MRI images.  ...  Here a brief review of different segmentation for detection of brain tumor from MRI of brain has been discussed.  ...  to detect or segment a brain tumor from MRI of brain image.  ... 
arXiv:1312.6150v1 fatcat:fspy3az4uffwjbwvjo75rjwdlu

Deep Learning Enables Automatic Detection and Segmentation of Brain Metastases on Multi-Sequence MRI [article]

Endre Grøvik, Darvin Yi, Michael Iv, Elisabeth Tong, Daniel L. Rubin, Greg Zaharchuk
2019 arXiv   pre-print
This study demonstrates automated detection and segmentation of brain metastases on multi-sequence MRI using a deep learning approach based on a fully convolution neural network (CNN).  ...  In conclusion, a deep learning approach using multi-sequence MRI can aid in the detection and segmentation of brain metastases.  ...  This study demonstrate automated detection and segmentation of brain metastases on multi-sequence MRI using a deep learning approach based on a fully convolution neural network (CNN).  ... 
arXiv:1903.07988v1 fatcat:igpmay2pzve3do4ctt26sn3ys4

Computer-Assisted Analysis of Biomedical Images [article]

Leonardo Rundo
2021 arXiv   pre-print
In this regard, frameworks based on advanced Machine Learning and Computational Intelligence can significantly improve traditional Image Processing and Pattern Recognition approaches.  ...  In conclusion, the ultimate goal of these research studies is to gain clinically and biologically useful insights that can guide differential diagnosis and therapies, leading towards biomedical data integration  ...  First of all, they use the multi-atlas segmentation approach in [327] for prostate segmentation, by extending it for using multi-parametric data.  ... 
arXiv:2106.04381v1 fatcat:osqiyd3sbja3zgrby7bf4eljfm
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