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Automated Segmentation of Cortical Grey Matter from T1-Weighted MRI Images

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Published on: January 7, 2019

A Review of U-Net Based Deep Learning Frameworks for MRI-Based Brain Tumor Segmentation.

Ayse Bastug Koc^1,2, Devrim Akgun³

¹Computer and Informatics Engineering Department, Institute of Natural Science and Technology, Sakarya University, Esentepe Campus, 54050 Serdivan, Sakarya, Türkiye.

Diagnostics (Basel, Switzerland)

|February 27, 2026

Summary

This review analyzes 35 studies on U-Net-based brain tumor segmentation using MRI scans. It details U-Net architecture advancements and their effectiveness in clinical applications.

Keywords:

BRATS dataset U-Net U-Net variants brain tumor segmentation glioma subregions

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Area of Science:

Medical Imaging
Artificial Intelligence
Computational Biology

Background:

Automated brain tumor segmentation from MRI is crucial for clinical applications.
Deep learning, particularly the U-Net architecture, has emerged as a leading approach.
Existing reviews lack a focused analysis of U-Net variants for brain tumor segmentation.

Purpose of the Study:

To review and analyze 35 studies (2019-2025) on U-Net-based brain tumor segmentation.
To examine the evolution of U-Net architectures from 2D to 3D and advanced variants.
To synthesize results, evaluation criteria, and benchmark datasets in this field.

Main Methods:

Systematic literature review of 35 studies published between 2019 and 2025.
Analysis of U-Net architecture modifications and adaptations for brain tumor segmentation.
Synthesis of reported results, evaluation metrics, and datasets (e.g., BRATS).

Main Results:

U-Net architectures have shown significant effectiveness in brain tumor segmentation tasks.
Various modifications enhance U-Net performance, addressing limitations of original 2D and 3D models.
Standard evaluation criteria and benchmark datasets are crucial for model validation.

Conclusions:

U-Net remains a dominant and evolving architecture for brain tumor segmentation.
Future research should focus on model efficiency, generalization, multimodal data integration, and clinical translation.
This review provides a comprehensive guide for researchers in the field.