DFP-ResUNet:Convolutional Neural Network with a Dilated Convolutional Feature Pyramid for Multimodal Brain Tumor Segmentation

Wang, Jingjing; Gao, Jun; Ren, Jinwen; Luan, Zhenye; Yu, Zishu; Yan-hua, Zhao; Zhao, Yuefeng

doi:10.1016/j.cmpb.2021.106208

Cited by 42 publications

(11 citation statements)

References 27 publications

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“…9, our proposed model achieved better segmentation performance than the other three models. [38] 0.8200 0.8500 0.7600 0.8400 0.9000 0.7900 0.8400 0.9100 0.8800 ---Aboelenein et al [39] 0.8520 0.8120 0.7410 0.8850 0.8200 0.7680 0.9980 0.9970 0.9990 8.2500 3.3010 3.3040 Zhou et al [40] 0.9121 0.8662 0.8181 0.9139 0.8558 0.8520 ---3.8800 6.8100 2.700 Wang et al [41] 0.8970 0.9060 0.8430 0.9150 0.9010 0.8760 0.9910 0.9980 0.9970 5.2300 6.3650 2.1990 Chen et al [42] 0.8600 0.7900 0.7500 0.9100 0.8600 0.8300 0.8400 0.7800 0.7400 ---Huang et al [43] 0…”

Section: Experiments Resultsmentioning

confidence: 99%

“…The decoder introduces residual blocks to avoid degradation problems. To improve the ability of the neural network to extract and utilize multiscale image features, Wang et al [41] proposed a spatial dilated feature pyramid (DFP) module. Most models cannot make full use of the global context information, so Chen et al [42] presented a two-stage automated brain lesion segmentation framework by integrating cascaded RF and dense CRF, which can effectively integrate the local appearance and global contextual information of multimodal MRI and iteratively improve the segmentation results.…”

Section: E Comparison With Other State-of-the-art Methodsmentioning

confidence: 99%

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SEResU-Net for Multimodal Brain Tumor Segmentation

et al. 2022

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Glioma is the most common type of brain tumor, and it has a high mortality rate. Accurate tumor segmentation based on magnetic resonance imaging (MRI) is of great significance for the diagnosis and treatment of brain tumors. Recently, a brain tumor segmentation method based on U-Net has gained considerable attention. However, brain tumor segmentation is a challenging task due to the structural variations and inhomogeneous intensity of tumors. Existing brain tumor segmentation studies have shown that the problems of insufficient down-sampling feature extraction and loss of up-sampling information arise when using U-Net to segment brain tumors. In this study, we proposed an improved U-Net model, SEResU-Net, which combines the deep residual network and the Squeeze-and-Excitation Network (SENet). The deep residual network solves the problem of network degradation so that SEResU-Net can extract more feature information. SENet avoids information loss and enables the network to focus on the useful feature map, which solves the problem of insufficient segmentation accuracy of small-scale brain tumors. Furthermore, we developed a fusion loss function, which is composed of Dice loss and cross-entropy loss, to solve the problems of network convergence and data imbalance. We evaluated the segmentation performance of SEResU-Net on BraTS2018 and BraTS2019, two authoritative MRI brain tumor benchmarks. Experimental results revealed that SEResU-Net outperformed SEU-Net, ResU-Net, and U-Net. For SEResU-Net, the mean Dice similarity coefficients were 0.9373, 0.9108, and 0.8758 for the whole tumor, the tumor core, and the enhanced tumor, which were 7.10%, 11.88%, and 15.33% greater than those of the U-Net benchmark network, respectively. Our findings demonstrate that the proposed SEResU-Net has a competitive effect in segmenting multimodal brain tumors.

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Section: Experiments Resultsmentioning

confidence: 99%

Section: E Comparison With Other State-of-the-art Methodsmentioning

confidence: 99%

SEResU-Net for Multimodal Brain Tumor Segmentation

et al. 2022

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“…Wang et al [21] proposed a segmentation network based on a segmented attention module, which extracted useful information in connected features through different attention mechanisms and discarded redundant information to realize selective aggregation of features. What is more, Wang et al [22] proposed extracting multi-scale image features by using a spatial module composed of multiple parallel dilated convolution layers and deepening the network structure by using a residual module. Shen et al [23] proposed a multitask full convolutional network for the automatic segmentation of brain tumors.…”

Section: Related Workmentioning

confidence: 99%

DRT-Unet: A Segmentation Network for Aiding Brain Tumor Diagnosis

2022

Security and Communication Networks

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Using image segmentation techniques to assist physicians in brain tumor diagnosis is a hot issue in computer technology research. Although most brain tumor segmentation networks to date have been based on U-Net, the prediction results are depending on which are not well generalized and need to be further improved. As the depth of the network increases, the gradients of the network vanish together with the decrease of the accuracy; meanwhile, the large number of parameters in the network will cause data redundancy. Moreover, a single modality of MRI images cannot adequately segment tumor details. Therefore, a segmentation network with an improved U-Net model is proposed in this paper, which combines Dilated Convolution-Dense Block-Transformation Convolution-Unet (hereafter referred to as DRT-Unet). The network adopts the combination of dilated convolution, dense residual block, and transposed convolution. In the coding process, a dilated convolution block and a local feature residual for fusing dense block are adopted to replace the 3 × 3 convolution layers on each layer in U-Net, and a transition layer is used for down-sampling. In the decoding process, a local feature residual is adopted for fusing dense blocks; meanwhile, a deconvolution structure with up-pooling and transposed convolution cascade is used. By connecting the decoded output features with the encoded low-level visual features, the information on transition layer loss is obtained. The experiments in this paper are carried out on BraTs2018 and BraTs2019 datasets; as a result, the DRT-Unet network can effectively segment tumor lesion regions.

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“…They used cropping, random slicing, and z -score normalization as the preprocessing methods. The authors in [ 149 ] proposed a novel architecture combining U-Net encoding and decoding subarchitecture, dilated convolutional feature extracting layers, and a residual module. Their proposed architecture achieved a dice score of 0.843, 0.897, and 0.906 and 0.798, 0.902, and 0.845 on ET, WT, and TC brain tumor subregions on BRATS 2018 and BRATS 2019 challenges, respectively.…”

Section: Current Applications Of Deep Learning In Cancer Diagnosis Prognosis and Predictionmentioning

confidence: 99%

Deep Learning in Cancer Diagnosis and Prognosis Prediction: A Minireview on Challenges, Recent Trends, and Future Directions

Tufail

Kaabar

et al. 2021

Computational and Mathematical Methods in Medicine

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Deep learning (DL) is a branch of machine learning and artificial intelligence that has been applied to many areas in different domains such as health care and drug design. Cancer prognosis estimates the ultimate fate of a cancer subject and provides survival estimation of the subjects. An accurate and timely diagnostic and prognostic decision will greatly benefit cancer subjects. DL has emerged as a technology of choice due to the availability of high computational resources. The main components in a standard computer-aided design (CAD) system are preprocessing, feature recognition, extraction and selection, categorization, and performance assessment. Reduction of costs associated with sequencing systems offers a myriad of opportunities for building precise models for cancer diagnosis and prognosis prediction. In this survey, we provided a summary of current works where DL has helped to determine the best models for the cancer diagnosis and prognosis prediction tasks. DL is a generic model requiring minimal data manipulations and achieves better results while working with enormous volumes of data. Aims are to scrutinize the influence of DL systems using histopathology images, present a summary of state-of-the-art DL methods, and give directions to future researchers to refine the existing methods.

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DFP-ResUNet:Convolutional Neural Network with a Dilated Convolutional Feature Pyramid for Multimodal Brain Tumor Segmentation

Cited by 42 publications

References 27 publications

SEResU-Net for Multimodal Brain Tumor Segmentation

SEResU-Net for Multimodal Brain Tumor Segmentation

DRT-Unet: A Segmentation Network for Aiding Brain Tumor Diagnosis

Deep Learning in Cancer Diagnosis and Prognosis Prediction: A Minireview on Challenges, Recent Trends, and Future Directions

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