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Related Experiment Video

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Modeling Brain Metastases Through Intracranial Injection and Magnetic Resonance Imaging

Modeling Brain Metastases Through Intracranial Injection and Magnetic Resonance Imaging

Published on: June 7, 2020

Recent deep learning-based brain tumor segmentation models using multi-modality magnetic resonance imaging: a

Zain Ul Abidin¹, Rizwan Ali Naqvi¹, Amir Haider¹

¹Department of Intelligent Mechatronics Engineering, Sejong University, Seoul, Republic of Korea.

Frontiers in Bioengineering and Biotechnology

|August 6, 2024

Summary

Deep learning models enhance brain tumor segmentation using multi-modal MRI scans. This AI-driven approach aids radiologists in diagnosis and personalized treatment planning for brain tumors.

Keywords:

brain tumor segmentation convolutional neural network deep learning medical images multi-modality analysis vision transformers

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Last Updated: Jun 17, 2025

Modeling Brain Metastases Through Intracranial Injection and Magnetic Resonance Imaging

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Published on: June 7, 2020

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Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

Published on: September 25, 2019

Area of Science:

Medical Imaging
Artificial Intelligence in Healthcare
Neuro-oncology

Background:

Brain tumor segmentation is critical for treatment planning but presents challenges for radiologists.
Artificial intelligence (AI), particularly deep learning (DL), offers advanced tools to assist in this diagnostic process.
Multi-modal magnetic resonance imaging (MRI) is increasingly utilized for detailed brain tumor analysis.

Purpose of the Study:

To survey recent deep learning (DL) models for brain tumor segmentation using multi-modal MRI.
To analyze current trends, datasets, and evaluation metrics in the field.
To identify challenges and suggest future research directions for improved diagnostic accuracy.

Main Methods:

Review of multi-modal MRI modalities and their characteristics.
Categorization and discussion of DL models based on architecture: Convolutional Neural Networks (CNNs), Vision Transformers, and hybrid models.
Statistical analysis of recent publications, datasets, and segmentation evaluation metrics.

Main Results:

Identified and categorized various DL architectures (CNN, Transformer, hybrid) for brain tumor segmentation.
Provided statistical insights into recent research, common datasets, and performance metrics.
Highlighted open challenges and potential future research avenues.

Conclusions:

Deep learning models show significant promise for improving brain tumor segmentation accuracy using multi-modal MRI.
Further research is needed to address current challenges and optimize AI tools for clinical application.
Advancements in AI-assisted segmentation can lead to better patient outcomes and personalized cancer care.