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Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...

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

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Published on: January 7, 2019

Efficient Brain Tumor Segmentation for MRI Images Using YOLO-BT.

Mengying Xiong¹, Aiping Wu², Yue Yang³

¹School of Electronic Information and Electrical Engineering, Yangtze University, Jingzhou 434023, China.

Sensors (Basel, Switzerland)

|June 27, 2025

Summary

This study introduces YOLO-BT, an improved brain tumor segmentation algorithm for MRI images. YOLO-BT enhances detection accuracy and efficiency for irregular tumors compared to existing methods.

Keywords:

YOLO-BT YOLOv11 brain tumor computer vision deep learning image processing

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Area of Science:

Medical Imaging
Artificial Intelligence
Computer Vision

Background:

Brain tumor segmentation in MRI images faces challenges due to irregular shapes and size variations, impacting accuracy and efficiency.
Existing algorithms struggle with precise detection and segmentation of complex brain tumors.

Purpose of the Study:

To develop an advanced brain tumor segmentation algorithm, YOLO-BT, to address limitations in accuracy and efficiency.
To improve the detection and segmentation of irregularly shaped and variably sized brain tumors in MRI scans.

Main Methods:

YOLO-BT utilizes UNetV2 as a backbone with an attention mechanism for enhanced feature extraction.
Incorporates BiFPN structure for bidirectional fusion of cross-scale features, replacing traditional splicing.
Integrates D-LKA mechanism with large convolution kernels to improve characterization of complex and irregular tumor structures.

Main Results:

YOLO-BT demonstrated significant improvements in Precision, Recall, mAP50, and mAP50-95 compared to YOLOv11 in both candidate box and mask-based evaluations.
Achieved a 6.1% increase in mean Intersection over Union (mIOU) and a 3.6% increase in Dice coefficient.
The algorithm shows enhanced ability to characterize tumors of different scales and irregular shapes.

Conclusions:

The proposed YOLO-BT algorithm is effective and suitable for brain tumor detection and segmentation in MRI images.
The integration of UNetV2, BiFPN, and D-LKA mechanisms significantly enhances segmentation performance for challenging brain tumor cases.