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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

Automated medical image segmentation techniques.

Neeraj Sharma¹, Lalit M Aggarwal

¹School of Biomedical Engineering, Institute of Technology, Institute of Medical Sciences, Banaras Hindu University, Varanasi-221 005, UP, India.

Journal of Medical Physics

|February 24, 2010

Summary

This summary is machine-generated.

Accurate medical image segmentation is crucial for radiotherapy planning. This review details automated methods for Computed Tomography (CT) and Magnetic Resonance (MR) imaging, analyzing their challenges and effectiveness.

Keywords:

Artificial intelligence techniques computed tomography magnetic resonance imaging medical images artifacts segmentation

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Area of Science:

Medical Imaging
Radiotherapy Planning
Image Segmentation

Background:

Accurate medical image segmentation is vital for radiotherapy planning.
Computed Tomography (CT) and Magnetic Resonance (MR) imaging are primary diagnostic and treatment planning tools.
Automated segmentation methods are increasingly important in clinical workflows.

Purpose of the Study:

To review automated segmentation methods for CT and MR images.
To identify challenges in medical image segmentation.
To compare the merits and limitations of existing segmentation techniques.

Main Methods:

Review of automated segmentation techniques for medical imaging.
Analysis of methods applied to CT and MR datasets.
Comparative evaluation of segmentation approaches.

Main Results:

Automated segmentation methods show promise but face challenges in CT and MR image analysis.
Different methods exhibit varying degrees of success and limitations.
Understanding these limitations is key to improving radiotherapy planning.

Conclusions:

Automated segmentation is a critical but complex aspect of radiotherapy planning.
Further research is needed to overcome segmentation challenges in CT and MR imaging.
Optimizing segmentation methods will enhance treatment accuracy and patient outcomes.