Brain Tumor Segmentation Using an Ensemble of 3D U-Nets and Overall Survival Prediction Using Radiomic Features

Feng, Xue; Tustison, Nicholas J.; Patel, Sohil H.; Meyer, Craig H.

doi:10.3389/fncom.2020.00025

Cited by 158 publications

(97 citation statements)

References 25 publications

(16 reference statements)

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“…Pawel et al [14] proposed a brain tumor segmentation method using a 3D-CNN, where 3D random patches are obtained and used for training and features extracted by 2D-CNNs (capturing a rich information from a long-range 2D context in three orthogonal directions) are used as an additional input to a 3D-CNN. A brain tumor segmentation method was proposed by using an ensemble of 3D U-Nets with different hyper-parameters trained on non-uniformly extracted patches in [15]. In [16], trained multiple deep neural networks with a 3D U-Net architecture in a tree structure to create segmentations for edema, non-enhancing tumor, and enhancing tumor regions.…”

Section: Related Studiesmentioning

confidence: 99%

“…The patch-wise splitting of a slice improves the localization accuracy in the MRI tissue segmentation because the trained network is designed for focusing more on local details in a patch. In contrast, randomly selected portions and/or regions from a slices or MRI volume are considered to be patches used for training the model in existing methods [11][12][13][14][15][16]. This is different from our approach, which divides a whole slice into a number of uniform-sized patches and feed the patches into the model for training.…”

Section: Introductionmentioning

confidence: 99%

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Automatic segmentation of brain MRI using a novel patch-wise U-net deep architecture

2020

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Accurate segmentation of brain magnetic resonance imaging (MRI) is an essential step in quantifying the changes in brain structure. Deep learning in recent years has been extensively used for brain image segmentation with highly promising performance. In particular, the U-net architecture has been widely used for segmentation in various biomedical related fields. In this paper, we propose a patch-wise U-net architecture for the automatic segmentation of brain structures in structural MRI. In the proposed brain segmentation method, the non-overlapping patch-wise U-net is used to overcome the drawbacks of conventional U-net with more retention of local information. In our proposed method, the slices from an MRI scan are divided into non-overlapping patches that are fed into the U-net model along with their corresponding patches of ground truth so as to train the network. The experimental results show that the proposed patch-wise U-net model achieves a Dice similarity coefficient (DSC) score of 0.93 in average and outperforms the conventional U-net and the SegNetbased methods by 3% and 10%, respectively, for on Open Access Series of Imaging Studies (OASIS) and Internet Brain Segmentation Repository (IBSR) dataset.

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Section: Related Studiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automatic segmentation of brain MRI using a novel patch-wise U-net deep architecture

2020

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“…The tumor segmentation maps extracted from the above methodology was used to extract texture and wavelet based features using the PyRadiomics (Van Griethuysen, Fedorov et al 2017) and Pywavelets (Lee, Gommers et al 2019) packages from each tumor subcomponent for each contrast. In addition, we added volume and surface area features of each tumor component (Feng, Tustison et al 2018) and age. We performed feature selection based on SelectKBest features using the sklearn package (Pedregosa, Varoquaux et al 2011, Buitinck, Louppe et al 2013) which resulted in a reduced set of 25 features.…”

Section: Ensemble Methodologymentioning

confidence: 99%

Multidimensional and Multiresolution Ensemble Networks for Brain Tumor Segmentation

Murugesan

Nalawade

Ganesh

et al. 2019

Preprint

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In this work, we developed multiple 2D and 3D segmentation models with multiresolution input to segment brain tumor components, and then ensembled them to obtain robust segmentation maps. This reduced overfitting and resulted in a more generalized model. Multiparametric MR images of 335 subjects from BRATS 2019 challenge were used for training the models. Further, we tested a classical machine learning algorithm (xgboost) with features extracted from the segmentation maps to classify subject survival range. Preliminary results on the BRATS 2019 validation dataset demonstrasted this method can achieve excellent performance with DICE scores of 0.898, 0.784, 0.779 for whole tumor, tumor core and enhancing tumor respectively and accuracy 34.5 % for survuval prediction. * -Equal Contribution

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“…The degree of angiogenesis was calculated by subtracting T1w and T1C in the tumor ROI, followed by a threshold of 50%. Finally, age and resection status were added to the feature set [34]. A total of 11,468 features were extracted combining the above features including imaging, texture and wavelet based features.…”

Section: Survival Analysismentioning

confidence: 99%

Fully Automated Brain Tumor Segmentation and Survival Prediction of Gliomas using Deep Learning and MRI

Yogananda

Nalawade

Murugesan

et al. 2019

Preprint

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Tumor segmentation of magnetic resonance (MR) images is a critical step in providing objective measures of predicting aggressiveness and response to therapy in gliomas. It has valuable applications in diagnosis, monitoring, and treatment planning of brain tumors. The purpose of this work was to develop a fully automated deep learning method for brain tumor segmentation and survival prediction. Well curated brain tumor cases with multi-parametric MR Images from the BraTS2019 dataset were used. A three-group framework was implemented, with each group consisting of three 3D-Dense-UNets to segment whole tumor (WT), tumor core (TC) and enhancing tumor (ET). This method was implemented to decompose the complex multi-class segmentation problem into individual binary segmentation problems for each sub-component. Each group was trained using different approaches and loss functions. The output segmentations of a particular label from their respective networks from the 3 groups were ensembled and post-processed. For survival analysis, a linear regression model based on imaging texture features and wavelet texture features extracted from each of the segmented components was implemented.The networks were tested on the BraTS2019 validation dataset including 125 cases for the brain tumor segmentation task and 29 cases for the survival prediction task. The segmentation networks achieved average dice scores of 0.901, 0.844 and 0.801 for WT, TC and ET respectively. The survival prediction network achieved an accuracy score of 0.55 and mean squared error (MSE) of 119244. This method could be implemented as a robust tool to assist clinicians in primary brain tumor management and follow-up.

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Brain Tumor Segmentation Using an Ensemble of 3D U-Nets and Overall Survival Prediction Using Radiomic Features

Cited by 158 publications

References 25 publications

Automatic segmentation of brain MRI using a novel patch-wise U-net deep architecture

Automatic segmentation of brain MRI using a novel patch-wise U-net deep architecture

Multidimensional and Multiresolution Ensemble Networks for Brain Tumor Segmentation

Fully Automated Brain Tumor Segmentation and Survival Prediction of Gliomas using Deep Learning and MRI

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