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Updated: Nov 5, 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

Deep Learning in Biomedical Optics.

Lei Tian¹, Brady Hunt², Muyinatu A Lediju Bell^3,4,5

¹Department of Electrical and Computer Engineering, Boston University, 8 St. Mary's Street, RM 830, Boston, Massachusetts, 02215.

Lasers in Surgery and Medicine

|May 20, 2021

Summary

This summary is machine-generated.

This review explores deep learning applications in biomedical optics, focusing on image formation across various medical imaging techniques. It highlights how artificial intelligence enhances optical methods in medicine, discussing challenges and future opportunities.

Keywords:

biomedical optics biophotonics computer aided detection deep learning diffuse tomography fluorescence lifetime functional optical brain imaging in vivo microscopy machine learning microscopy optical coherence tomography photoacoustic imaging widefield endoscopy

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Area of Science:

Biomedical optics
Medical imaging
Artificial intelligence

Background:

Biomedical optics is crucial for medical diagnostics and research.
Advancements in imaging technologies are continuously needed.
Deep learning offers powerful tools for image analysis and formation.

Purpose of the Study:

To review deep learning applications in biomedical optics, specifically for image formation.
To organize applications by imaging domains within biomedical optics.
To identify how deep learning enables new capabilities in medical optics.

Main Methods:

Literature review of deep learning applications in various biomedical optics imaging domains.
Categorization of methods by imaging type, including microscopy, OCT, and photoacoustic imaging.
Summary of deep learning's role in enhancing image formation and capabilities.

Main Results:

Deep learning is increasingly applied across microscopy, OCT, photoacoustic imaging, and brain imaging.
AI methods enable novel capabilities in image resolution, reconstruction, and analysis.
Significant progress has been made in leveraging deep learning for optical imaging.

Conclusions:

Deep learning is transforming biomedical optics, particularly in image formation.
Further research and development are needed to overcome challenges in translation and adoption.
AI holds substantial promise for advancing optical technologies in medicine.