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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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Updated: Dec 11, 2025

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

[Research on brain image segmentation based on deep learning].

Yuli Wang¹, Zijian Zhao¹

¹School of Control Science and Engineering, Shandong University, Jinan 250061, P.R.China.

Sheng Wu Yi Xue Gong Cheng Xue Za Zhi = Journal of Biomedical Engineering = Shengwu Yixue Gongchengxue Zazhi

|August 26, 2020

Summary

This summary is machine-generated.

Deep learning significantly advances brain image segmentation. This review details current deep learning segmentation algorithms, offering insights into future research directions for improved brain imaging analysis.

Keywords:

brain image segmentation convolutional neural network deep learning general deep learning models prior knowledge

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Area of Science:

Neuroimaging
Artificial Intelligence
Medical Image Analysis

Background:

Brain image segmentation is crucial for understanding neurological conditions.
Traditional methods face limitations in accuracy and efficiency.
Deep learning offers promising advancements in this field.

Purpose of the Study:

To systematically review deep learning-based brain image segmentation algorithms.
To highlight the advantages of deep learning in this domain.
To provide a comprehensive overview of current research progress.

Main Methods:

Categorization of algorithms based on existing brain image problems.
Exploration of algorithms guided by prior knowledge.
Analysis of general deep learning model applications in brain segmentation.

Main Results:

Deep learning algorithms demonstrate superior performance in brain image segmentation.
A systematic classification of current deep learning approaches is presented.
Key challenges and advancements are identified.

Conclusions:

Deep learning is a pivotal technology for brain image segmentation.
Further research directions are proposed to enhance algorithm development.
This review serves as a guide for researchers in the field.