PDF] Brain Tumor Segmentation of MRI Images Using Processed Image Driven U-Net Architecture
Por um escritor misterioso
Last updated 08 setembro 2024
![PDF] Brain Tumor Segmentation of MRI Images Using Processed Image Driven U-Net Architecture](https://d3i71xaburhd42.cloudfront.net/c750894747d2b3f841de55922b2b68794295de27/7-Table3-1.png)
A fully automatic methodology to handle the task of segmentation of gliomas in pre-operative MRI scans is developed using a U-Net-based deep learning model that reached high-performance accuracy on the BraTS 2018 training, validation, as well as testing dataset. Brain tumor segmentation seeks to separate healthy tissue from tumorous regions. This is an essential step in diagnosis and treatment planning to maximize the likelihood of successful treatment. Magnetic resonance imaging (MRI) provides detailed information about brain tumor anatomy, making it an important tool for effective diagnosis which is requisite to replace the existing manual detection system where patients rely on the skills and expertise of a human. In order to solve this problem, a brain tumor segmentation & detection system is proposed where experiments are tested on the collected BraTS 2018 dataset. This dataset contains four different MRI modalities for each patient as T1, T2, T1Gd, and FLAIR, and as an outcome, a segmented image and ground truth of tumor segmentation, i.e., class label, is provided. A fully automatic methodology to handle the task of segmentation of gliomas in pre-operative MRI scans is developed using a U-Net-based deep learning model. The first step is to transform input image data, which is further processed through various techniques—subset division, narrow object region, category brain slicing, watershed algorithm, and feature scaling was done. All these steps are implied before entering data into the U-Net Deep learning model. The U-Net Deep learning model is used to perform pixel label segmentation on the segment tumor region. The algorithm reached high-performance accuracy on the BraTS 2018 training, validation, as well as testing dataset. The proposed model achieved a dice coefficient of 0.9815, 0.9844, 0.9804, and 0.9954 on the testing dataset for sets HGG-1, HGG-2, HGG-3, and LGG-1, respectively.
![PDF] Brain Tumor Segmentation of MRI Images Using Processed Image Driven U-Net Architecture](https://www.mdpi.com/computers/computers-10-00139/article_deploy/html/images/computers-10-00139-g003.png)
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![PDF] Brain Tumor Segmentation of MRI Images Using Processed Image Driven U-Net Architecture](https://onlinelibrary.wiley.com/cms/asset/85e7cbd1-a542-4c37-9d7f-16c756eeef98/ima22571-fig-0008-m.jpg)
International Journal of Imaging Systems and Technology, IMA
![PDF] Brain Tumor Segmentation of MRI Images Using Processed Image Driven U-Net Architecture](https://www.researchgate.net/publication/349902492/figure/fig1/AS:1024432529215497@1621255156793/Proposed-tumor-segmentation-and-classification-architecture.png)
Proposed tumor segmentation and classification architecture
![PDF] Brain Tumor Segmentation of MRI Images Using Processed Image Driven U-Net Architecture](https://media.springernature.com/lw685/springer-static/image/art%3A10.1186%2Fs13244-020-00869-4/MediaObjects/13244_2020_869_Fig5_HTML.png)
Convolutional neural networks for brain tumour segmentation, Insights into Imaging
![PDF] Brain Tumor Segmentation of MRI Images Using Processed Image Driven U-Net Architecture](https://file.techscience.com/ueditor/files/cmes/TSP_CMES_128-2/TSP_CMES_14107/TSP_CMES_14107/Images/CMES_14107-fig-5.png/mobile_webp)
MRI Brain Tumor Segmentation Using 3D U-Net with Dense Encoder Blocks and Residual Decoder Blocks
![PDF] Brain Tumor Segmentation of MRI Images Using Processed Image Driven U-Net Architecture](https://media.springernature.com/lw685/springer-static/image/art%3A10.1038%2Fs41598-021-90428-8/MediaObjects/41598_2021_90428_Fig12_HTML.jpg)
Brain tumor segmentation based on deep learning and an attention mechanism using MRI multi-modalities brain images
![PDF] Brain Tumor Segmentation of MRI Images Using Processed Image Driven U-Net Architecture](https://media.springernature.com/m685/springer-static/image/art%3A10.1038%2Fs41598-023-47107-7/MediaObjects/41598_2023_47107_Fig1_HTML.png)
Utilizing deep learning via the 3D U-net neural network for the delineation of brain stroke lesions in MRI image
![PDF] Brain Tumor Segmentation of MRI Images Using Processed Image Driven U-Net Architecture](https://www.sciltp.com/journals/public/site/images/ijndi/pic/173-3.jpg)
Deep Learning Attention Mechanism in Medical Image Analysis: Basics and Beyonds-Scilight
![PDF] Brain Tumor Segmentation of MRI Images Using Processed Image Driven U-Net Architecture](https://ietresearch.onlinelibrary.wiley.com/cms/asset/783f8011-bdec-422a-a43e-d006814a5ad8/ipr212219-fig-0002-m.jpg)
Brain tumour cell segmentation and detection using deep learning networks - Bagyaraj - 2021 - IET Image Processing - Wiley Online Library
How U and W Net Architecture in Computer Vision shaped some real work problems in Medical
![PDF] Brain Tumor Segmentation of MRI Images Using Processed Image Driven U-Net Architecture](https://www.degruyter.com/document/doi/10.1515/jisys-2022-0206/asset/graphic/j_jisys-2022-0206_fig_003.jpg)
A novel deep learning-based brain tumor detection using the Bagging ensemble with K-nearest neighbor
GitHub - IAmSuyogJadhav/3d-mri-brain-tumor-segmentation-using-autoencoder-regularization: Keras implementation of the paper 3D MRI brain tumor segmentation using autoencoder regularization by Myronenko A. (
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Brain Tumor Segmentation
![PDF] Brain Tumor Segmentation of MRI Images Using Processed Image Driven U-Net Architecture](https://www.mdpi.com/computers/computers-10-00139/article_deploy/html/images/computers-10-00139-g004.png)
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