Improved U-Net3+ with Stage Residual for Brain Tumor Segmentation

Abstract Background For the coding part of U-Net3+, the brain tumor feature extraction ability is insufficient, leading to insufficient feature fusion when sampling on the network and reducing the segmentation accuracy. Methods In this study, we propose an improved U-Net3+ segmentation network based on stage residual. In the encoder part, the encoder based on the stage residual structure is used to reduce the degradation problem caused by the increase in network depth and enhance the feature extraction ability of the encoder, which is convenient for full feature fusion when sampling on the network. Besides, we used a filter response normalization (FRN) layer instead of a batch normalization layer to eliminate batch size impact on the network. Based on the improved U-Net3+ two-dimensional (2D) model with stage residual, IResUnet3+ three-dimensional (3D) model is constructed. We explore appropriate methods to deal with 3D data, which achieve accurate segmentation of the 3D network. Results The experimental results showed that: the sensitivity of WT, TC, and ET increased by 1.34%, 4.6%, and 8.44%, respectively. And the Dice coefficients of ET and WT were further increased by 3.43% and 1.03%, respectively. To facilitate further research, source code can be found at: https://github.com/YuOnlyLookOne/IResUnet3Plus. Conclusion In the segmentation task of brain tumor brats2018 dataset, compared with the classical networks u-net, v-net, resunet and u-net3 +, the proposed network has smaller parameters and significantly improved accuracy.

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Recurrent Multi-Fiber Network for 3D MRI Brain Tumor Segmentation

Symmetry ◽

10.3390/sym13020320 ◽

2021 ◽

Vol 13 (2) ◽

pp. 320

Author(s):

Yue Zhao ◽

Xiaoqiang Ren ◽

Kun Hou ◽

Wentao Li

Keyword(s):

Brain Tumor ◽

Network Architecture ◽

Contextual Information ◽

Computational Cost ◽

Disease Diagnosis ◽

Tumor Segmentation ◽

Brain Tumor Segmentation ◽

Fiber Network ◽

3D Network ◽

The Brain

Automated brain tumor segmentation based on 3D magnetic resonance imaging (MRI) is critical to disease diagnosis. Moreover, robust and accurate achieving automatic extraction of brain tumor is a big challenge because of the inherent heterogeneity of the tumor structure. In this paper, we present an efficient semantic segmentation 3D recurrent multi-fiber network (RMFNet), which is based on encoder–decoder architecture to segment the brain tumor accurately. 3D RMFNet is applied in our paper to solve the problem of brain tumor segmentation, including a 3D recurrent unit and 3D multi-fiber unit. First of all, we propose that recurrent units segment brain tumors by connecting recurrent units and convolutional layers. This quality enhances the model’s ability to integrate contextual information and is of great significance to enhance the contextual information. Then, a 3D multi-fiber unit is added to the overall network to solve the high computational cost caused by the use of a 3D network architecture to capture local features. 3D RMFNet combines both advantages from a 3D recurrent unit and 3D multi-fiber unit. Extensive experiments on the Brain Tumor Segmentation (BraTS) 2018 challenge dataset show that our RMFNet remarkably outperforms state-of-the-art methods, and achieves average Dice scores of 89.62%, 83.65% and 78.72% for the whole tumor, tumor core and enhancing tumor, respectively. The experimental results prove our architecture to be an efficient tool for brain tumor segmentation accurately.

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Learning rich features with hybrid loss for brain tumor segmentation

BMC Medical Informatics and Decision Making ◽

10.1186/s12911-021-01431-y ◽

2021 ◽

Vol 21 (S2) ◽

Author(s):

Daobin Huang ◽

Minghui Wang ◽

Ling Zhang ◽

Haichun Li ◽

Minquan Ye ◽

...

Keyword(s):

Feature Extraction ◽

Brain Tumor ◽

Class Imbalance ◽

Feature Representation ◽

Loss Functions ◽

Radiotherapy Planning ◽

Tumor Segmentation ◽

Brain Tumor Segmentation ◽

Scale Feature ◽

Multi Scale

Abstract Background Accurately segment the tumor region of MRI images is important for brain tumor diagnosis and radiotherapy planning. At present, manual segmentation is wildly adopted in clinical and there is a strong need for an automatic and objective system to alleviate the workload of radiologists. Methods We propose a parallel multi-scale feature fusing architecture to generate rich feature representation for accurate brain tumor segmentation. It comprises two parts: (1) Feature Extraction Network (FEN) for brain tumor feature extraction at different levels and (2) Multi-scale Feature Fusing Network (MSFFN) for merge all different scale features in a parallel manner. In addition, we use two hybrid loss functions to optimize the proposed network for the class imbalance issue. Results We validate our method on BRATS 2015, with 0.86, 0.73 and 0.61 in Dice for the three tumor regions (complete, core and enhancing), and the model parameter size is only 6.3 MB. Without any post-processing operations, our method still outperforms published state-of-the-arts methods on the segmentation results of complete tumor regions and obtains competitive performance in another two regions. Conclusions The proposed parallel structure can effectively fuse multi-level features to generate rich feature representation for high-resolution results. Moreover, the hybrid loss functions can alleviate the class imbalance issue and guide the training process. The proposed method can be used in other medical segmentation tasks.

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Mesh free alternate directional implicit method based three dimensional super-diffusive model for benign brain tumor segmentation

Computers & Mathematics with Applications ◽

10.1016/j.camwa.2019.02.009 ◽

2019 ◽

Vol 77 (12) ◽

pp. 3212-3223 ◽

Cited By ~ 8

Author(s):

Saroj Kumar Chandra ◽

Manish Kumar Bajpai

Keyword(s):

Brain Tumor ◽

Three Dimensional ◽

Implicit Method ◽

Tumor Segmentation ◽

Brain Tumor Segmentation ◽

Diffusive Model ◽

Mesh Free

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A Review On The Techniques Of Brain Tumor: Segmentation, Feature Extraction And Classification

2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT) ◽

10.1109/icccnt49239.2020.9225472 ◽

2020 ◽

Author(s):

G. Yogalakshmi ◽

B.Sheela Rani

Keyword(s):

Feature Extraction ◽

Brain Tumor ◽

Tumor Segmentation ◽

Brain Tumor Segmentation

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Patient Specific Brain Tumor Segmentation using Context Sensitive Feature Extraction in MR Images

International Journal of Computing and Digital Systems ◽

10.12785/ijcds/090607 ◽

2020 ◽

Vol 09 (6) ◽

pp. 1091-1097

Author(s):

Nisha Joseph ◽

D Murugan ◽

Basil John Thomas ◽

Ramya A

Keyword(s):

Feature Extraction ◽

Brain Tumor ◽

Patient Specific ◽

Tumor Segmentation ◽

Mr Images ◽

Brain Tumor Segmentation ◽

Context Sensitive

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SGPNet: A Three-Dimensional Multitask Residual Framework for Segmentation and IDH Genotype Prediction of Gliomas

Computational Intelligence and Neuroscience ◽

10.1155/2021/5520281 ◽

2021 ◽

Vol 2021 ◽

pp. 1-9

Author(s):

Yao Wang ◽

Yan Wang ◽

Chunjie Guo ◽

Shuangquan Zhang ◽

Lili Yang

Keyword(s):

Brain Tumor ◽

Clinical Decision Making ◽

Three Dimensional ◽

Clinical Decision ◽

Error Rates ◽

The Cancer Genome Atlas ◽

Classification Error ◽

Tumor Segmentation ◽

Brain Tumor Segmentation ◽

The Status

Glioma is the main type of malignant brain tumor in adults, and the status of isocitrate dehydrogenase (IDH) mutation highly affects the diagnosis, treatment, and prognosis of gliomas. Radiographic medical imaging provides a noninvasive platform for sampling both inter and intralesion heterogeneity of gliomas, and previous research has shown that the IDH genotype can be predicted from the fusion of multimodality radiology images. The features of medical images and IDH genotype are vital for medical treatment; however, it still lacks a multitask framework for the segmentation of the lesion areas of gliomas and the prediction of IDH genotype. In this paper, we propose a novel three-dimensional (3D) multitask deep learning model for segmentation and genotype prediction (SGPNet). The residual units are also introduced into the SGPNet that allows the output blocks to extract hierarchical features for different tasks and facilitate the information propagation. Our model reduces 26.6% classification error rates comparing with previous models on the datasets of Multimodal Brain Tumor Segmentation Challenge (BRATS) 2020 and The Cancer Genome Atlas (TCGA) gliomas’ databases. Furthermore, we first practically investigate the influence of lesion areas on the performance of IDH genotype prediction by setting different groups of learning targets. The experimental results indicate that the information of lesion areas is more important for the IDH genotype prediction. Our framework is effective and generalizable, which can serve as a highly automated tool to be applied in clinical decision making.

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Brain Tumor Segmentation and Classification using Multiple Feature Extraction and Convolutional Neural Networks

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.f2948.0810621 ◽

2021 ◽

Vol 10 (6) ◽

pp. 23-27

Author(s):

Tasmiya Tazeen ◽

◽

Mrinal Sarvagya ◽

Keyword(s):

Magnetic Resonance Imaging ◽

Feature Extraction ◽

Brain Tumor ◽

Magnetic Resonance ◽

Early Detection ◽

Tumor Segmentation ◽

Resonance Imaging ◽

Brain Tumor Segmentation ◽

Segmentation Accuracy ◽

Magnetic Resonance Imaging Mri

Intracranial tumors are a type of cancer that grows spontaneously inside the skull. Brain tumor is the cause for one in four deaths. Hence early detection of the tumor is important. For this aim, a variety of segmentation techniques are available. The fundamental disadvantage of present approaches is their low segmentation accuracy. With the help of magnetic resonance imaging (MRI), a preventive medical step of early detection and evaluation of brain tumor is done. Magnetic resonance imaging (MRI) offers detailed information on human delicate tissue, which aids in the diagnosis of a brain tumor. The proposed method in this paper is Brain Tumour Detection and Classification based on Ensembled Feature extraction and classification using CNN.

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