RMU-Net: A Novel Residual Mobile U-Net Model for Brain Tumor Segmentation from MR Images

The most aggressive form of brain tumor is gliomas, which leads to concise life when high grade. The early detection of glioma is important to save the life of patients. MRI is a commonly used approach for brain tumors evaluation. However, the massive amount of data provided by MRI prevents manual segmentation in a reasonable time, restricting the use of accurate quantitative measurements in clinical practice. An automatic and reliable method is required that can segment tumors accurately. To achieve end-to-end brain tumor segmentation, a hybrid deep learning model RMU-Net is proposed. The architecture of MobileNetV2 is modified by adding residual blocks to learn in-depth features. This modified Mobile Net V2 is used as an encoder in the proposed network, and upsampling layers of U-Net are used as the decoder part. The proposed model has been validated on BraTS 2020, BraTS 2019, and BraTS 2018 datasets. The RMU-Net achieved the dice coefficient scores for WT, TC, and ET of 91.35%, 88.13%, and 83.26% on the BraTS 2020 dataset, 91.76%, 91.23%, and 83.19% on the BraTS 2019 dataset, and 90.80%, 86.75%, and 79.36% on the BraTS 2018 dataset, respectively. The performance of the proposed method outperforms with less computational cost and time as compared to previous methods.

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Multi-Task Deep Supervision on Attention R2U-Net for Brain Tumor Segmentation

Frontiers in Oncology ◽

10.3389/fonc.2021.704850 ◽

2021 ◽

Vol 11 ◽

Author(s):

Shiqiang Ma ◽

Jijun Tang ◽

Fei Guo

Keyword(s):

Deep Learning ◽

Brain Tumor ◽

Computational Cost ◽

Medical Image Segmentation ◽

Validation Dataset ◽

Tumor Segmentation ◽

Brain Tumor Segmentation ◽

Proposed Model ◽

Fusion Methods ◽

Tumor Area

Accurate automatic medical image segmentation technology plays an important role for the diagnosis and treatment of brain tumor. However, simple deep learning models are difficult to locate the tumor area and obtain accurate segmentation boundaries. In order to solve the problems above, we propose a 2D end-to-end model of attention R2U-Net with multi-task deep supervision (MTDS). MTDS can extract rich semantic information from images, obtain accurate segmentation boundaries, and prevent overfitting problems in deep learning. Furthermore, we propose the attention pre-activation residual module (APR), which is an attention mechanism based on multi-scale fusion methods. APR is suitable for a deep learning model to help the network locate the tumor area accurately. Finally, we evaluate our proposed model on the public BraTS 2020 validation dataset which consists of 125 cases, and got a competitive brain tumor segmentation result. Compared with the state-of-the-art brain tumor segmentation methods, our method has the characteristics of a small parameter and low computational cost.

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A Novel Deep Learning Model DDU-net Using Edge Features to Enhance Brain Tumor Segmentation on MR images

Artificial Intelligence in Medicine ◽

10.1016/j.artmed.2021.102180 ◽

2021 ◽

pp. 102180

Author(s):

Min Jiang ◽

Fuhao Zhai ◽

Jun Kong

Keyword(s):

Deep Learning ◽

Brain Tumor ◽

Learning Model ◽

Tumor Segmentation ◽

Mr Images ◽

Brain Tumor Segmentation ◽

Edge Features ◽

Deep Learning Model

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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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Improving Brain Tumor Segmentation in Multi-sequence MR Images Using Cross-Sequence MR Image Generation

Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries - Lecture Notes in Computer Science ◽

10.1007/978-3-030-46643-5_3 ◽

2020 ◽

pp. 27-36

Author(s):

Guojing Zhao ◽

Jianpeng Zhang ◽

Yong Xia

Keyword(s):

Brain Tumor ◽

Tumor Segmentation ◽

Image Generation ◽

Mr Images ◽

Mr Image ◽

Brain Tumor Segmentation

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Automated Early Detection of Astrocytomas Tumor Based on Optimized Adaptive Cluster with Super Pixel Model

International Journal of Artificial Intelligence Tools ◽

10.1142/s0218213021400170 ◽

2021 ◽

Vol 30 (06n08) ◽

Author(s):

V. K. Deepak ◽

R. Sarath

Keyword(s):

Brain Tumor ◽

Medical Image ◽

Automatic Segmentation ◽

Tumor Segmentation ◽

Low Grade ◽

Grey Wolf ◽

Dice Coefficient ◽

Grey Wolf Optimization ◽

Brain Tumor Segmentation ◽

Better Than

In the medical image-processing field brain tumor segmentation is aquintessential task. Thereby early diagnosis gives us a chance of increasing survival rate. It will be way much complex and time consuming when comes to processing large amount of MRI images manually, so for that we need an automatic way of brain tumor image segmentation process. This paper aims to gives a comparative study of brain tumor segmentation, which are MRI-based. So recent methods of automatic segmentation along with advanced techniques gives us an improved result and can solve issue better than any other methods. Therefore, this paper brings comparative analysis of three models such as Deformable model of Fuzzy C-Mean clustering (DMFCM), Adaptive Cluster with Super Pixel Segmentation (ACSP) and Grey Wolf Optimization based ACSP (GWO_ACSP) and these are tested on CANCER IMAGE ACHRCHIEVE which is a preparation information base containing High Grade and Low-Grade astrocytoma tumors. Here boundaries including Accuracy, Dice coefficient, Jaccard score and MCC are assessed and along these lines produce the outcomes. From this examination the test consequences of Grey Wolf Optimization based ACSP (GWO_ACSP) gives better answer for mind tumor division issue.

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