PDF] Brain Tumor Segmentation of MRI Images Using Processed Image Driven U-Net Architecture
Por um escritor misterioso
Last updated 26 outubro 2024
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.
Comparing 3D, 2.5D, and 2D Approaches to Brain Image Segmentation
SynthSR: A public AI tool to turn heterogeneous clinical brain scans into high-resolution T1-weighted images for 3D morphometry
PDF] Attention Gate ResU-Net for Automatic MRI Brain Tumor Segmentation
PDF] Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images
Computers, Free Full-Text
Improving brain tumor segmentation on MRI based on the deep U-net and residual units - IOS Press
3-D Brain Tumor Segmentation Using Deep Learning - MATLAB & Simulink Example
Deep-learning-based synthesis of post-contrast T1-weighted MRI for tumour response assessment in neuro-oncology: a multicentre, retrospective cohort study - The Lancet Digital Health
3D MRI Brain tumor segmentation, U-NET
U-Net-Based Medical Image Segmentation
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