Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Jan 14, 2026

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

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

Published on: January 7, 2019

Enhancing brain tumor segmentation using attention based convolutional UNet on MRI images.

Mohammad Abrar¹, Abdu Salam², Faizan Ullah³

¹Faculty of Computer Studies, Arab Open University, 122, Muscat, P.O. Box 1596, Oman. abrar.m@aou.edu.om.

Scientific Reports

|October 21, 2025

Summary

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

RUL-DBRS: A Novel Energy Efficient and Robust Protocol for Enhanced Communication in Underwater Wireless Sensor Networks.

Scientific reports·2026

Same author

Secure IIoT architecture with blockchain-enabled anomaly detection for water distribution cyber-physical systems.

Scientific reports·2026

Same author

Computational approaches for drug-drug interaction prediction: a systematic review of data sources, modeling strategies, and evaluation frameworks.

Frontiers in pharmacology·2026

Same author

Deep learning-based region merging with adaptive threshold optimization for building segmentation in remote sensing images.

PloS one·2026

Same author

Heuristic optimization of multiplierless decimation filter for multi-standard wireless applications.

Scientific reports·2026

Same author

LLM-guided population-based reinforcement learning: A scalable methodology for adaptive hyperparameter optimization.

MethodsX·2026

Attention-based Convolutional U-Net (ACU-Net) improves brain tumor segmentation accuracy on MRI scans. This AI model enhances precision and reliability for clinical applications, outperforming traditional methods.

Area of Science:

Medical Imaging
Artificial Intelligence
Computational Biology

Background:

Accurate brain tumor segmentation is critical for diagnosis and treatment planning.
Current automated methods struggle with complex tumor shapes, while manual segmentation is time-consuming and subjective.
Existing deep learning models like U-Nets and CNNs have limitations in capturing intricate tumor boundaries.

Purpose of the Study:

To propose and evaluate an Attention-based Convolutional U-Net (ACU-Net) model for enhanced brain tumor segmentation on MRI data.
To improve the precision and dependability of tumor edge delineation using attention mechanisms within a U-Net architecture.
To quantitatively assess the performance of ACU-Net against baseline models using standard segmentation metrics.

Main Methods:

Keywords:

Attention-based convolutional U-Net Brain tumor Deep learning MRI Medical imaging Segmentation Tumor boundary delineation

More Related Videos

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

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

Published on: November 30, 2022

Related Experiment Videos

Last Updated: Jan 14, 2026

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

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

Published on: January 7, 2019

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

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

Published on: November 30, 2022

Utilized the BraTS 2018 MRI dataset for brain tumor segmentation.
Preprocessed data through normalization, spatial resolution adjustment, and augmentation.
Developed the ACU-Net model incorporating attention gates, trained using dice and cross-entropy loss functions.
Compared ACU-Net performance with U-Nets and Convolutional Neural Networks (CNNs) using precision, recall, Dice Similarity Coefficient (DSC), and Intersection over Union (IoU).

Main Results:

ACU-Net achieved high Dice Similarity Coefficients: 94.04% for Whole Tumor (WT), 98.63% for Tumor Core (TC), and 98.77% for Enhancing Tumor (ET).
The proposed ACU-Net model demonstrated superior performance compared to baseline U-Net and CNN models.
Attention mechanisms significantly improved the accuracy and robustness of medical image segmentation for brain tumors.

Conclusions:

The ACU-Net model offers a reliable and effective tool for precise brain tumor segmentation in clinical settings.
Attention mechanisms are crucial for enhancing the performance of deep learning models in medical image analysis.
The study highlights the potential of ACU-Net to overcome limitations of existing segmentation methods, improving diagnostic and therapeutic planning.