Brain Tumor Segmentation and Tractographic Feature Extraction from Structural MR Images for Overall Survival Prediction

Kao, Po-Yu; Ngo, Thuyen; Zhang, Angela; Chen, Jefferson; Manjunath, B.S.

doi:10.1007/978-3-030-11726-9_12

Cited by 73 publications

(63 citation statements)

References 25 publications

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“…Therefore, our method is a much more efficient algorithm yet can achieve comparable segmentation accuracy. We also show a visual comparison of the brain tumor segmentation results of various methods including 3D UNet [1], Kao et al [16] and our DMFNet in Fig. 3.…”

Section: Experimental Results and Analysismentioning

confidence: 99%

3D Dilated Multi-fiber Network for Real-Time Brain Tumor Segmentation in MRI

Chen

Liu

Ding³

et al. 2019

Lecture Notes in Computer Science

136

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Brain tumor segmentation plays a pivotal role in medical image processing. In this work, we aim to segment brain MRI volumes. 3D convolution neural networks (CNN) such as 3D U-Net [1] and V-Net [2] employing 3D convolutions to capture the correlation between adjacent slices have achieved impressive segmentation results. However, these 3D CNN architectures come with high computational overheads due to multiple layers of 3D convolutions, which may make these models prohibitive for practical large-scale applications. To this end, we propose a highly efficient 3D CNN to achieve real-time dense volumetric segmentation. The network leverages the 3D multi-fiber unit which consists of an ensemble of lightweight 3D convolutional networks to significantly reduce the computational cost. Moreover, 3D dilated convolutions are used to build multi-scale feature representation. Extensive experimental results on the BraTS-2018 challenge dataset show that the proposed architecture greatly reduces computation cost while maintaining high accuracy for brain tumor segmentation. The source code is available at https://github.com/China-LiuXiaopeng/BraTS-DMFNet Keywords: 3D Brain tumor segmentation · 3D Multi-fiber unit · 3D dilate convolution · light-weight network.Recent advances in the treatment of gliomas have increased the demands on using magnetic resonance imaging (MRI) techniques for the diagnosis, tumor monitoring, and patient outcome prediction. Accurate segmentation of brain tumor is critical for diagnosis and treatment planning. However, automated brain tumor segmentation in multi-modal MRI scans is a challenging task due to the heterogeneous appearance and shape of gliomas [3].

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Section: Experimental Results and Analysismentioning

confidence: 99%

3D Dilated Multi-fiber Network for Real-Time Brain Tumor Segmentation in MRI

Chen

Liu

Ding³

et al. 2019

Lecture Notes in Computer Science

136

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“…BS refers to batch size. S/M/L refers to the small/medium/large batch size, indicating batch size of (1, 16, 32), (1,8,16), (1,16,32) for the RV, aorta, LV respectively. LR refers to the learning rate.…”

Section: Resultsmentioning

confidence: 99%

Normalization in Training U-Net for 2-D Biomedical Semantic Segmentation

Zhou

Yang

2019

IEEE Robot. Autom. Lett.

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2D biomedical semantic segmentation is important for robotic vision in surgery. Segmentation methods based on Deep Convolutional Neural Network (DCNN) can out-perform conventional methods in terms of both accuracy and levels of automation. One common issue in training a DCNN for biomedical semantic segmentation is the internal covariate shift where the training of convolutional kernels is encumbered by the distribution change of input features, hence both the training speed and performance are decreased. Batch Normalization (BN) is the first proposed method for addressing internal covariate shift and is widely used. Instance Normalization (IN) and Layer Normalization (LN) have also been proposed. Group Normalization (GN) is proposed more recently and has not yet been applied to 2D biomedical semantic segmentation 1 . Most DCNNs for biomedical semantic segmentation adopt BN as the normalization method by default, without reviewing its performance. In this paper, four normalization methods -BN, IN, LN and GN are compared in details, specifically for 2D biomedical semantic segmentation. U-Net is adopted as the basic DCNN structure. Three datasets regarding the Right Ventricle (RV), aorta, and Left Ventricle (LV) are used for the validation. The results show that detailed subdivision of the feature map, i.e. GN with a large group number or IN, achieves higher accuracy. This accuracy improvement mainly comes from better model generalization. Codes are uploaded and maintained at Xiao-Yun Zhou's Github.

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“…Table IV compares the accuracy of the proposed method with other state-of-the-art techniques. Methods proposed in [13], [14] uses the dataset of BraTS 2017 whereas other methods [19]- [22], [25], [26] use BraTS 2018 dataset. In [19], classifier training set is made up of the images with GTR status.…”

Section: Resultsmentioning

confidence: 99%

“…Classifier(s) Accuracy% [13] Ensemble of random forest and multi layer perceptron 52.6 [14] Linear Discriminant 46 [19] Linear SVM GTR set 63 [20] Neural network and random forest 38 [21] Artificial neural network 54.5 [22] Multi layer perceptron 50.8 [25] XGBoost 65 [26] Ensemble of random forest and regression network 47.5…”

Section: Refmentioning

confidence: 99%

Prediction of Overall Survival of Brain Tumor Patients

Agravat

Raval

2019

TENCON 2019 - 2019 IEEE Region 10 Conference (TENCON)

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Automated brain tumor segmentation plays an important role in the diagnosis and prognosis of the patient. In addition, features from the tumorous brain help in predicting patients' overall survival. The main focus of this paper is to segment tumor from BRATS 2018 benchmark dataset and use age, shape and volumetric features to predict overall survival of patients. The random forest classifier achieves overall survival accuracy of 59% on the test dataset and 67% on the dataset with resection status as gross total resection. The proposed approach uses fewer features but achieves better accuracy than state-ofthe-art methods.

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Brain Tumor Segmentation and Tractographic Feature Extraction from Structural MR Images for Overall Survival Prediction

Cited by 73 publications

References 25 publications

3D Dilated Multi-fiber Network for Real-Time Brain Tumor Segmentation in MRI

3D Dilated Multi-fiber Network for Real-Time Brain Tumor Segmentation in MRI

Normalization in Training U-Net for 2-D Biomedical Semantic Segmentation

Prediction of Overall Survival of Brain Tumor Patients

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