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Related Experiment Video

Updated: Dec 23, 2025

Quantification of Vascular Parameters in Whole Mount Retinas of Mice with Non-Proliferative and Proliferative Retinopathies

Quantification of Vascular Parameters in Whole Mount Retinas of Mice with Non-Proliferative and Proliferative Retinopathies

Published on: March 12, 2022

Automatic protocol for quantifying the vasoconstriction in blood vessel images.

Xuelin Xu^1,2, Lisheng Lin¹, Buhong Li¹

¹MOE Key Laboratory of OptoElectronic Science and Technology for Medicine, Fujian Provincial Key Laboratory for Photonics Technology, Fujian Normal University, Fuzhou, 350117, China.

Biomedical Optics Express

|April 29, 2020

Summary

A new automated method quantifies blood vessel constriction after vascular targeted photodynamic therapy (V-PDT) in mice. This technique precisely measures V-PDT effects, aiding in optimizing treatments and understanding mechanisms.

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Last Updated: Dec 23, 2025

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Retinal Vascular Reactivity as Assessed by Optical Coherence Tomography Angiography

Retinal Vascular Reactivity as Assessed by Optical Coherence Tomography Angiography

Published on: March 26, 2020

Area of Science:

Biomedical Engineering
Photodynamic Therapy
Vascular Biology

Background:

Vascular targeted photodynamic therapy (V-PDT) is used for vascular diseases.
Optimizing V-PDT requires understanding its effects on blood vessels.
The dorsal skin-fold window chamber (DSWC) model in mice is crucial for in vivo V-PDT research.

Purpose of the Study:

To develop an automated protocol for quantifying blood vessel vasoconstriction after V-PDT.
To accurately measure V-PDT-induced changes in blood vessels within the DSWC model.
To provide a tool for optimizing V-PDT protocols and elucidating biological mechanisms.

Main Methods:

Utilized the DSWC model in nude mice for in vivo V-PDT studies.
Developed an automated protocol tracking pixel changes in pre- and post-V-PDT images.
Employed Hough transform for region extraction, U-Net for image segmentation, and Speeded-Up Robust Features for image registration.

Main Results:

Achieved high accuracy (90.64%), sensitivity (80.12%), and specificity (92.83%) with the U-Net model for image segmentation.
Successfully quantified vasoconstriction by tracking disappearing pixels, indicating constricted blood vessels.
Observed a significant difference in vasoconstriction between V-PDT and control groups.

Conclusions:

The proposed automated protocol accurately quantifies blood vessel vasoconstriction in the DSWC model.
This method offers a reliable tool for V-PDT research, avoiding potential issues like new capillary generation.
The protocol has potential applications in optimizing clinical V-PDT treatments and advancing mechanistic studies.