Brain tumour segmentation using U-Net based fully convolutional networks and extremely randomized trees

Le, Hai Thanh; Pham, Thi-Thu-Hien

doi:10.31276/vjste.60(3).19

Cited by 16 publications

(9 citation statements)

References 14 publications

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“…The proposed segmentation results are considered good as compared to existing techniques. In (Le & Pham, ) accuracy is achieved of 85% and sensitivity of 87% on BRATS 2013 whereas our method gives results more than 90%.…”

Section: Resultsmentioning

confidence: 80%

Brain tumor detection and classification: A framework of marker‐based watershed algorithm and multilevel priority features selection

Khan

Lali

Rehman

et al. 2019

Microscopy Res & Technique

149

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Brain tumor identification using magnetic resonance images (MRI) is an important research domain in the field of medical imaging. Use of computerized techniques helps the doctors for the diagnosis and treatment against brain cancer. In this article, an automated system is developed for tumor extraction and classification from MRI. It is based on marker‐based watershed segmentation and features selection. Five primary steps are involved in the proposed system including tumor contrast, tumor extraction, multimodel features extraction, features selection, and classification. A gamma contrast stretching approach is implemented to improve the contrast of a tumor. Then, segmentation is done using marker‐based watershed algorithm. Shape, texture, and point features are extracted in the next step and high ranked 70% features are only selected through chi‐square max conditional priority features approach. In the later step, selected features are fused using a serial‐based concatenation method before classifying using support vector machine. All the experiments are performed on three data sets including Harvard, BRATS 2013, and privately collected MR images data set. Simulation results clearly reveal that the proposed system outperforms existing methods with greater precision and accuracy.

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Section: Resultsmentioning

confidence: 80%

Brain tumor detection and classification: A framework of marker‐based watershed algorithm and multilevel priority features selection

Khan

Lali

Rehman

et al. 2019

Microscopy Res & Technique

149

View full text Add to dashboard Cite

show abstract

“…A multiple convoluational layers with different dilation rates are used in parallel to obtain multi-scale features. Authors in [10] proposed UNet based fully convolutional networks for brain tumor segmentation. After applying CNN, extremely randamized trees classifier is used to segment the different classes of tumor.…”

Section: Related Workmentioning

confidence: 99%

Hybrid-DANet: An Encoder-Decoder Based Hybrid Weights Alignment with Multi-Dilated Attention Network for Automatic Brain Tumor Segmentation

Ilyas¹,

Song²,

Lee³

2022

Preprint

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“…Pham [14], suggested full-convolution U-Net networks acquire features from a multimodal MRI training dataset and then apply Extremely Randomized Trees (Extra-Trees) to segment the abnormal tumor cells. Wang et al .…”

Section: Related Workmentioning

confidence: 99%

HTTU-Net: Hybrid Two Track U-Net for Automatic Brain Tumor Segmentation

et al. 2020

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Brain cancer is one of the most dominant causes of cancer death; the best way to diagnose and treat brain tumors is to screen early. Magnetic Resonance Imaging (MRI)is commonly used for brain tumor diagnosis; however, it is challenging problem to achieve higher accuracy and performance , which is a vital problem in most of the previous presented automated medical diagnosis. In this paper, we propose a Hybrid Two-Track U-Net(HTTU-Net) architecture for brain tumor segmentation. This architecture leverages the use of Leaky Relu activation and batch normalization. It includes two tracks; each one has a different number of layers and utilizes a different kernel size. Then, we merge these two tracks to generate the final segmentation. We use the focal loss, and generalized Dice (GDL), loss functions to address the problem of class imbalance. The proposed segmentation method was evaluated on the BraTS'2018 datasets and obtained a mean Dice similarity coefficient of 0.865 for the whole tumor region, 0.808 for the core region and 0.745 for the enhancement region and a median Dice similarity coefficient of 0.883, 0.895, and 0.815 for the whole tumor, core and enhancing region, respectively. The proposed HTTU-Net architecture is sufficient for the segmentation of brain tumors and achieves highly accurate results. Other quantitative and qualitative evaluation are discussed along the paper it confirms that our results are very comparable expert human-level performance, and could help experts to decrease the time of diagnostic. INDEX TERMS Brain Tumor Segmentation, deep neural networks, U-net, fully convolutional network, BraTS'2018 challenge.

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Brain tumour segmentation using U-Net based fully convolutional networks and extremely randomized trees

Cited by 16 publications

References 14 publications

Brain tumor detection and classification: A framework of marker‐based watershed algorithm and multilevel priority features selection

Brain tumor detection and classification: A framework of marker‐based watershed algorithm and multilevel priority features selection

Hybrid-DANet: An Encoder-Decoder Based Hybrid Weights Alignment with Multi-Dilated Attention Network for Automatic Brain Tumor Segmentation

HTTU-Net: Hybrid Two Track U-Net for Automatic Brain Tumor Segmentation

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