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Related Experiment Video

Updated: Jun 9, 2026

Alignment of Visible-Light Optical Coherence Tomography Fibergrams with Confocal Images of the Same Mouse Retina

Alignment of Visible-Light Optical Coherence Tomography Fibergrams with Confocal Images of the Same Mouse Retina

Published on: June 30, 2023

Combined image-processing algorithms for improved optical coherence tomography of prostate nerves.

Shahab Chitchian¹, Thomas P Weldon, Michael A Fiddy

¹University of North Carolina at Charlotte, Department of Physics and Optical Science, Charlotte, North Carolina 28223, USA. schitchi@uncc.edu

Journal of Biomedical Optics

|August 31, 2010

Summary

This study enhances cavernous nerve identification in rat prostates using advanced imaging algorithms. Improved visualization aids nerve-sparing prostate cancer surgery.

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Last Updated: Jun 9, 2026

Alignment of Visible-Light Optical Coherence Tomography Fibergrams with Confocal Images of the Same Mouse Retina

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3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography

3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography

Published on: October 24, 2019

Area of Science:

Biomedical Engineering
Surgical Technology
Medical Imaging

Background:

Cavernous nerves are crucial for erectile function and are vulnerable during prostate cancer surgery.
Accurate identification of these nerves is essential for nerve-sparing procedures.

Purpose of the Study:

To develop and evaluate image processing algorithms for improved identification of cavernous nerves in rat prostate tissue.
To enhance the visualization of delicate nerve structures during surgical planning.

Main Methods:

Application of segmentation algorithms to differentiate nerves from prostate tissue.
Utilizing a dual-tree complex wavelet transform for locally adaptive denoising to reduce speckle noise.
Employing edge detection for deeper imaging penetration of the prostate gland.

Main Results:

Achieved improved signal-to-noise ratio and imaging depth in optical coherence tomography (OCT) images.
Demonstrated automatic identification of cavernous nerves with enhanced clarity.
The combined algorithms significantly improved the visualization of nerve structures.

Conclusions:

The integrated image processing approach effectively enhances cavernous nerve identification in OCT images.
This technique shows potential for direct application in improving nerve-sparing outcomes in laparoscopic and robotic prostate cancer surgery.