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Fibril-associated Collagen

Fibril-associated Collagen

Fibril-associated collagens are a type of collagens present in the extracellular matrix with interrupted triple helices or FACIT (Fibril-associated collagens interrupted triple-helices). FACIT help connect and attach the collagen fibrils with each other as well as with other proteins of the extracellular matrix.
For example, the type II collagen fibrils in cartilage have covalently bound type IX fibril-associated collagens at regular intervals. Other types of fibril-associated collagens are...

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Related Experiment Video

Updated: Dec 22, 2025

Quantifying Fibrillar Collagen Organization with Curvelet Transform-Based Tools

Quantifying Fibrillar Collagen Organization with Curvelet Transform-Based Tools

Published on: November 11, 2020

Fibrillar Collagen Quantification With Curvelet Transform Based Computational Methods.

Yuming Liu¹, Adib Keikhosravi^1,2, Carolyn A Pehlke^1,2

¹Laboratory for Optical and Computational Instrumentation, University of Wisconsin-Madison, Madison, WI, United States.

Frontiers in Bioengineering and Biotechnology

|May 7, 2020

Summary

This summary is machine-generated.

We developed advanced software using the curvelet transform (CT) for quantifying collagen fiber organization. This tool enhances disease biomarker discovery in cancer by improving collagen image analysis and fiber tracking.

Keywords:

breast cancer collagen organization curvelet transform fibrillar collagen image analysis software pancreatic cancer second harmonic generation microscopy tumor microenvironment

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Published on: April 9, 2018

Area of Science:

Biomedical Engineering
Computational Biology
Medical Imaging

Background:

Fibrillar collagen organization is crucial in diseases like breast and pancreatic cancer.
Image-based biomarkers for collagen topology are needed for disease diagnosis and prognosis.
Existing methods for collagen quantification lack advanced features for detailed analysis.

Purpose of the Study:

To present an enhanced software platform for collagen quantification based on the curvelet transform (CT).
To introduce new features for improved collagen fiber analysis, visualization, and validation.
To demonstrate the software's utility in cancer pathology and preclinical models.

Main Methods:

Utilized the curvelet transform (CT) algorithm for multiscale image representation, denoising, and edge enhancement.
Developed C++ code optimization for rapid individual fiber tracking and a Java-based synthetic fiber generator for validation.
Implemented automatic tumor boundary generation, parallel computing for batch processing, and region-of-interest analysis.

Main Results:

Validated individual fiber tracking and orientation accuracy using synthesized data.
Compared fiber orientation calculations with existing methods on pancreatic tissue, showing competitive performance.
Demonstrated automatic tumor boundary creation and collagen fiber alignment quantification in human breast cancer and in vivo mouse xenograft models.

Conclusions:

The CT-based software platform offers a robust and efficient solution for quantifying collagen organization.
New features enhance accuracy, speed, and applicability in diverse cancer research settings.
The tool shows significant potential for identifying novel image-based biomarkers in cancer diagnostics.