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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

A review on brain tumor segmentation based on deep learning methods with federated learning techniques.

Md Faysal Ahamed¹, Md Munawar Hossain², Md Nahiduzzaman²

¹Department of Computer Science & Engineering, Rajshahi University of Engineering & Technology, Rajshahi 6204, Bangladesh.

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society

|November 27, 2023

Summary

Automated deep learning methods offer a faster, more accurate alternative to manual brain tumor segmentation. This review surveys effective techniques and federated learning approaches for improved performance and privacy in medical imaging.

Keywords:

BraTS Brain tumor Deep learning Federated learning Fusion mechanism Modality Segmentation

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Last Updated: Jul 9, 2025

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Area of Science:

Medical Imaging
Artificial Intelligence
Computer Vision

Background:

Manual brain tumor segmentation is time-consuming, error-prone, and costly.
Deep learning shows promise for automated segmentation in medical imaging.
Accurate brain tumor segmentation is crucial for diagnosis and treatment planning.

Purpose of the Study:

To review effective deep learning-based brain tumor segmentation techniques.
To survey federated learning methodologies for enhanced segmentation performance and privacy.
To identify current challenges and future research directions in brain tumor segmentation.

Main Methods:

Comprehensive literature review of over 100 papers on brain tumor segmentation.
Analysis of widely used and publicly available datasets.
Survey of federated learning approaches for multi-modal medical image analysis.

Main Results:

Identified effective deep learning segmentation techniques.
Highlighted the potential of federated learning for privacy-preserving, collaborative model training.
Summarized current challenges including data heterogeneity and model generalizability.

Conclusions:

Deep learning offers significant advantages for automated brain tumor segmentation.
Federated learning presents a promising avenue for improving global segmentation accuracy while preserving patient privacy.
Further research is needed to address unsolved problems and optimize federated model training strategies.