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Related Experiment Video

Updated: Jul 30, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Published on: November 30, 2022

U-Net-Based Models towards Optimal MR Brain Image Segmentation.

Rammah Yousef¹, Shakir Khan^2,3, Gaurav Gupta¹

¹Yogananda School of AI, Computers and Data Sciences, Shoolini University, Solan 173229, India.

Diagnostics (Basel, Switzerland)

|May 13, 2023

Summary

This summary is machine-generated.

This study reviews U-Net architecture advancements for brain tumor segmentation in MRI scans. Experiments show U-Net variants offer promising performance improvements for this challenging task.

Keywords:

MR brain images U-Net deep learning image segmentation loss functions machine learning optimization

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Deep Learning-Based Segmentation of Cryo-Electron Tomograms

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Published on: November 11, 2022

Area of Science:

Medical Imaging
Artificial Intelligence
Neuroscience

Background:

Brain tumor segmentation from MRI is a complex task for radiologists.
Automated systems are needed for accurate and generalized brain tumor segmentation.
U-Net based deep learning models are prevalent in medical image segmentation.

Purpose of the Study:

To review advancements and trends in U-Net architectures for brain tumor segmentation.
To quantitatively compare U-Net architectures for performance and optimization evolution.
To experimentally evaluate U-Net variants on the BraTS 2020 dataset.

Main Methods:

Literature review of U-Net architecture innovations.
Quantitative comparison of U-Net model performance.
Experimental evaluation of 3D U-Net, Attention U-Net, R2 Attention U-Net, and modified 3D U-Net on the BraTS 2020 dataset.
Performance assessment using Dice score and Hausdorff distance 95%.

Main Results:

U-Net architectures demonstrate significant potential for enhancing brain tumor segmentation.
The study provides a comparative overview of different U-Net models' performance.
Experimental results highlight the effectiveness of specific U-Net variants on the BraTS 2020 dataset.

Conclusions:

U-Net based models are crucial for advancing automated brain tumor segmentation.
Further research into novel architectures is important for overcoming medical image analysis challenges.
Optimization strategies are key to improving the performance of deep learning models in neuroimaging.