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The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)
2015
IEEE Transactions on Medical Imaging
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Abstract-In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences. ...
In order to gauge the current state-of-the-art in automated brain tumor segmentation and compare between different methods, we organized in 2012 and 2013 a Multimodal Brain Tumor Image Segmentation Benchmark ...
doi:10.1109/tmi.2014.2377694
pmid:25494501
pmcid:PMC4833122
fatcat:csrnfqc4i5eilh7wk5howvpr4u
Brain tumor segmentation with self-ensembled, deeply-supervised 3D U-net neural networks: a BraTS 2020 challenge solution
[article]
2020
arXiv
pre-print
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2020 pre-print arXiv:2011.01045v1 fatcat:ljx7yeih3vfutmbpq4co557ooy
In order to automate and standardize this task, we trained multiple U-net like neural networks, mainly with deep supervision and stochastic weight averaging, on the Multimodal Brain Tumor Segmentation ...
Brain tumor segmentation is a critical task for patient's disease management. ...
Multimodal Brain Tumor Segmentation challenge 2020 The Multimodal Brain Tumor Segmentation Challenge 2020 [3] [4] [5] [6] [7] was split in three different tasks: segmentation of the different tumor sub-regions ...
arXiv:2011.01045v2
fatcat:u2iztls35ffm5idh5xqv5o5j24
MICCAI Brain Tumor Segmentation (BraTS) 2020 Benchmark: "Prediction of Survival and Pseudoprogression"
[article]
2020
Zenodo
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This is the challenge design document for the "MICCAI Brain Tumor Segmentation (BraTS) 2020 Benchmark: 'Prediction of Survival and Pseudoprogression' ", accepted for MICCAI 2020. ...
BraTS 2020 utilizes multi-institutional MRI scans and focuses on the segmentation of intrinsically heterogeneous (in appearance, shape, and histology) brain tumors, namely gliomas. ...
MICCAI Brain Tumor Segmentation (BraTS) 2020 Benchmark: "Prediction of Survival and Pseudoprogression" Uncertainty Quantification. ...
doi:10.5281/zenodo.3718904
fatcat:v2uczjou35g4zi4caxkeauseh4
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
[article]
2019
arXiv
pre-print
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This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation ...
Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. ...
These data contributors are: 1) Center for Biomedical Image Computing and Analytics ( We would also like to thank the sponsorship offered by the CBICA@UPenn for the plaques provided to the top-ranked ...
arXiv:1811.02629v3
fatcat:ngweji7ynfhm5dfcdyfqtb5ntm
Robust and Accurate Automated Methods for Detection and Segmentation of Brain Tumor in MRI
2019
International journal of recent technology and engineering
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Finally, an assessment and evaluation of the state-of-the-art brain tumor segmentation methods are presented and future directions to improve and standardize the detection and segmentation of brain tumor ...
over segmentation in brain MR images. ...
Fig. 5 . 5 Processing and Enhancement of the input multimodal brain MRI
Fig. 7 .Fig. 8 . 78 on multimodal brain MRI and benchmark multimodal brain MRI datasets. ...
doi:10.35940/ijrte.d9129.118419
fatcat:obhzm7m7g5ef3lh7qpoxjtnl64
Automatic Brain Tumor Detection and Segmentation Using U-Net Based Fully Convolutional Networks
[article]
2017
arXiv
pre-print
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Our method was evaluated on Multimodal Brain Tumor Image Segmentation (BRATS 2015) datasets, which contain 220 high-grade brain tumor and 54 low-grade tumor cases. ...
In this context, a reliable fully automatic segmentation method for the brain tumor segmentation is necessary for an efficient measurement of the tumor extent. ...
In addition, a dedicated annual workshop and challenge, namely Multimodal Brain Tumor Image Segmentation (BRATS), is held to benchmark different algorithms that developed for the brain tumor segmentation ...
arXiv:1705.03820v3
fatcat:5yrppohivncwpapwqjeq5ki2d4
Improving 3D U-Net for Brain Tumor Segmentation by Utilizing Lesion Prior
[article]
2020
arXiv
pre-print
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The VOI map is then integrated with the multimodal MR images and input to a 3D U-Net for segmentation. ...
We propose a novel, simple and effective method to integrate lesion prior and a 3D U-Net for improving brain tumor segmentation. ...
Convolutional neural networks have achieved state-of-the-art performance in the recent Multimodal Brain Tumor Image Segmentation Benchmarks (BraTS) [6, 7, 9, 15] . ...
arXiv:1907.00281v3
fatcat:z7dt424iwneovh35doe3ytqksy
Fast and Accurate Semi-Automatic Segmentation Tool for Brain Tumor MRIs
[article]
2017
arXiv
pre-print
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Segmentation, the process of delineating tumor apart from healthy tissue, is a vital part of both the clinical assessment and the quantitative analysis of brain cancers. ...
Here, we provide an open-source algorithm (MITKats), built on the Medical Imaging Interaction Toolkit, to provide user-friendly and expedient tools for semi-automatic segmentation. ...
obtained from the Multimodal Brain Tumor Segmentation Challenge (BRATS) 2012 (Menze et al., 2015) , a notable accuracy benchmark. ...
arXiv:1705.06823v1
fatcat:axx332gidrcgtfzcpd6hg7arqq
Rank-Two NMF Clustering for Glioblastoma Characterization
2018
Journal of Healthcare Engineering
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Quantitative and qualitative evaluations over the publicly training and testing dataset from the MICCAI 2015 multimodal brain segmentation challenge (BraTS 2015) attested that the proposed algorithm could ...
In the segmentation process, we apply a rank-two NMF clustering. We have executed our tumor characterization method on BraTS 2015 challenge dataset. ...
Benchmark (BraTS 2015). e Multimodal Brain Tumor Segmentation dataset (BraTs 2015) is in continuation of BraTS 2012, BraTS 2013, and BraTS 2014. It has been organized by B. Menze, M. ...
doi:10.1155/2018/1048164
pmid:30425818
pmcid:PMC6218733
fatcat:crcxqxcuvzfsnjmfathyapb3ty
A Novel Domain Adaptation Framework for Medical Image Segmentation
[article]
2018
arXiv
pre-print
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multimodal MR images with known segmentation. ...
We demonstrate the performance improvement using a 2D U-Net for the BraTS'18 segmentation challenge. ...
The winning algorithm of the Multimodal Brain Tumor Image Segmentation Benchmark (BraTS) challenge in 2017 was based on Ensembles of Multiple Models and Architectures (EMMA) [10] , which bagged a heterogeneous ...
arXiv:1810.05732v1
fatcat:3k2s34yh2nc6dgdql2lmrxal64
Multimodal Self-Supervised Learning for Medical Image Analysis
[article]
2020
arXiv
pre-print
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We showcase our approach on four downstream tasks: Brain tumor segmentation and survival days prediction using four MRI modalities, Prostate segmentation using two MRI modalities, and Liver segmentation ...
We introduce the multimodal puzzle task, which facilitates rich representation learning from multiple image modalities. ...
Datasets In our experiments, we consider three multimodal medical imaging datasets. The first is the Multimodal Brain Tumor Image Segmentation Benchmark (BraTS) dataset [83] , [84] . ...
arXiv:1912.05396v2
fatcat:klwchysg4ney3phlmlawyrzxvi
Brain tumor classification from multi-modality MRI using wavelets and machine learning
2017
Pattern Analysis and Applications
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The data from multimodal brain tumor segmentation challenge (MICCAI BraTS 2013) are utilized which are co-registered and skullstripped, and the histogram matching is performed with a reference volume of ...
In this paper, we propose a brain tumor segmentation and classification method for multi-modality magnetic resonance imaging scans. ...
MRI provides the ability to capture multiple images known as multimodality images, which can provide the detailed structure of brain to efficiently classify the brain tumor [1] . ...
doi:10.1007/s10044-017-0597-8
fatcat:cvru7t7ex5exvdkblqahpmojom
TranSiam: Fusing Multimodal Visual Features Using Transformer for Medical Image Segmentation
[article]
2022
arXiv
pre-print
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On the BraTS 2019 and BraTS 2020 multimodal datasets, we have a significant improvement in accuracy over other popular methods. ...
To solve these problems, we propose a segmentation method suitable for multimodal medical images that can capture global information, named TranSiam. ...
The BraTS 2019 training dataset consists of 335 3D brain MR images. Both BraTS 2019 and 2020 validation sets contain 125 3D brain MR images. ...
arXiv:2204.12185v1
fatcat:gavcsgaov5hppmtp2xh74egmda
Attention Gate ResU-Net for automatic MRI brain tumor segmentation
2020
IEEE Access
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We extensively evaluate attention gate units on three authoritative MRI brain tumor benchmarks, i.e., BraTS 2017, BraTS 2018 and BraTS 2019. ...
Brain tumor segmentation technology plays a pivotal role in the process of diagnosis and treatment of MRI brain tumors. ...
evaluate the effectiveness of attention gate and AGResU-Net on three benchmarks of BraTS 2017, BraTS 2018 and BraTS 2019 from Multimodal Brain Tumor Segmentation Challenge (BraTS). ...
doi:10.1109/access.2020.2983075
fatcat:cowon2jf6va5pfge4f72774ixe
Robust Multimodal Brain Tumor Segmentation via Feature Disentanglement and Gated Fusion
[article]
2020
arXiv
pre-print
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We validate our method on the important yet challenging multimodal brain tumor segmentation task with the BRATS challenge dataset. ...
Accurate medical image segmentation commonly requires effective learning of the complementary information from multimodal data. ...
We validate our proposed method on the task of multimodal brain tumor segmentation with BRATS challenge [10] . ...
arXiv:2002.09708v1
fatcat:bfwb7tlljbcpnao4evye34xr3i
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