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A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment

A Label-Free Segmentation Approach for Intravital Imaging of Mammary Tumor Microenvironment

Published on: May 24, 2022

A modular supervised algorithm for vessel segmentation in red-free retinal images.

Andrea Anzalone¹, Federico Bizzarri, Mauro Parodi

¹Biophysical and Electronic Engineering Department, University of Genoa, Via Opera Pia 11a, I-16145 Genova, Italy.

Computers in Biology and Medicine

|July 16, 2008

Summary

This summary is machine-generated.

A new supervised algorithm accurately segments retinal vessels in red-free images. This method optimizes parameters using performance measures, achieving high accuracy (0.9587 MAA) and specificity (0.9477 SP) with efficient processing.

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Retinal Vascular Reactivity as Assessed by Optical Coherence Tomography Angiography

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Last Updated: Mar 2, 2026

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Retinal Vascular Reactivity as Assessed by Optical Coherence Tomography Angiography

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Published on: March 26, 2020

Area of Science:

Ophthalmology
Medical Imaging
Computer Vision

Background:

Accurate segmentation of retinal blood vessels is crucial for diagnosing various eye conditions.
Existing methods may lack efficiency or accuracy in segmenting vessels from red-free retinal images.

Purpose of the Study:

To propose a novel supervised algorithm for robust vessel segmentation in human retinal red-free images.
To optimize algorithm parameters using quantitative performance measures for tailored feature emphasis.

Main Methods:

Development of a modular supervised algorithm comprising two fundamental blocks.
Optimization of algorithm parameters by maximizing Measures of Performance (MOPs) for quantitative evaluation.
Comparative analysis of the proposed algorithm's performance against existing literature methods.

Main Results:

The proposed algorithm demonstrates a favorable balance between segmentation quality and processing speed.
Achieved maximum average accuracy (MAA) of 0.9587, K value of 0.8069, and specificity (SP) of 0.9477.
The MOP selection allows customization for emphasizing specific image features.

Conclusions:

The developed supervised algorithm offers an effective solution for retinal vessel segmentation in red-free images.
The method provides high accuracy and specificity while maintaining efficient processing times.
Parameter optimization using MOPs enhances the algorithm's adaptability and performance.