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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 Stimulation (TMS).

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Artificial Intelligence-Based Approaches for Brain Tumor Segmentation in MRI: A Review.

Khadija Bibi¹, Mehmood Nawaz^1,2, Sheheryar Khan³

¹Department of Biomedical Engineering, The Chinese University of Hong Kong, Sha Tin, Hong Kong SAR, China.

NMR in Biomedicine

|September 17, 2025

Summary

Artificial intelligence, including convolutional neural networks (CNNs) and vision transformers (ViTs), offers automated brain tumor segmentation from MRI scans. These AI methods enhance early diagnosis and treatment by improving tumor identification speed and accuracy.

Keywords:

brain tumor segmentation computed tomography convolution neural networks deep learning foundation models machine learning magnetic resonance imaging transformers

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Area of Science:

Medical Image Analysis
Artificial Intelligence in Oncology
Neuroimaging Techniques

Background:

Manual brain tumor segmentation in MRI is labor-intensive and requires extensive expertise.
Automated segmentation using AI is vital for early tumor detection, diagnosis, and treatment planning.
Advancements in machine learning and deep learning have spurred AI-driven solutions for brain tumor segmentation.

Purpose of the Study:

To review MRI techniques and popular approaches for brain tumor segmentation.
To highlight recent advancements in automated brain tumor segmentation.
To identify challenges and future research directions in the field.

Main Methods:

Comprehensive literature review of over 200 scholarly publications.
Analysis of artificial intelligence techniques, including convolutional neural networks (CNNs) and vision transformers (ViTs).
Examination of network architecture design, segmentation in unbalanced datasets, and multi-modality approaches.

Main Results:

CNN-based methods and hybrid approaches demonstrate exceptional performance in segmenting brain tumors from MRI data.
AI techniques significantly improve the speed and accuracy of tumor identification, type, size, and location assessment.
The study synthesizes key findings from recent research in automated brain tumor segmentation.

Conclusions:

AI-driven brain tumor segmentation is a rapidly advancing field with significant clinical implications.
CNNs and hybrid models are highly effective for MRI-based brain tumor segmentation.
Further research is needed to address existing challenges and explore novel techniques for improved segmentation accuracy and efficiency.