PDF] Brain Tumor Segmentation of MRI Images Using Processed Image Driven U-Net Architecture
Por um escritor misterioso
Last updated 24 março 2025
![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.rsipvision.com/wp-content/uploads/2018/08/Joint_reconstruction_and_segmentation_2-1.png)
Deep Learning in Brain Imaging - Medical Image Analysis by RSIP Vision
![PDF] Brain Tumor Segmentation of MRI Images Using Processed Image Driven U-Net Architecture](https://d3i71xaburhd42.cloudfront.net/d12c02378276e1fb4f8dd00fd8b25e9761866a56/3-Figure1-1.png)
PDF] Attention Gate ResU-Net for Automatic MRI Brain Tumor Segmentation
![PDF] Brain Tumor Segmentation of MRI Images Using Processed Image Driven U-Net Architecture](https://file.techscience.com/ueditor/files/iasc/TSP_IASC-32-1/TSP_IASC_21206/TSP_IASC_21206/Images/IASC_21206-fig-2.png/mobile_webp)
Optimized U-Net Segmentation and Hybrid Res-Net for Brain Tumor MRI Images Classification
![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%2Fs41598-021-90428-8/MediaObjects/41598_2021_90428_Fig1_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://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://media.springernature.com/m685/springer-static/image/art%3A10.1038%2Fs41598-021-90428-8/MediaObjects/41598_2021_90428_Fig13_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://onlinelibrary.wiley.com/cms/asset/5ce591f4-73ba-4767-ae94-0f4897f4cc2a/ima22571-fig-0001-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://media.arxiv-vanity.com/render-output/7558552/graph1.jpg)
BiTr-Unet: a CNN-Transformer Combined Network for MRI Brain Tumor Segmentation – arXiv Vanity
![PDF] Brain Tumor Segmentation of MRI Images Using Processed Image Driven U-Net Architecture](https://spj.science.org/cms/10.34133/2021/8786793/asset/2f1c90e0-bd86-4a4e-a000-8f5b8ba0b4b0/assets/graphic/8786793.fig.001.jpg)
Advances in Deep Learning-Based Medical Image Analysis
![PDF] Brain Tumor Segmentation of MRI Images Using Processed Image Driven U-Net Architecture](https://theaisummer.com/static/14e9373847e5a2d6acc23bfbf8b76b9d/14b42/medical-image.jpg)
Deep learning in medical imaging - 3D medical image segmentation with PyTorch
![PDF] Brain Tumor Segmentation of MRI Images Using Processed Image Driven U-Net Architecture](https://ijisae.org/public/journals/1/submission_2610_2894_coverImage_en_US.png)
Absolute Structure Threshold Segmentation Technique Based Brain Tumor Detection Using Deep Belief Convolution Neural Classifier
![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
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