Name: quantifying fibrillar collagen organization with curvelet transform-based tools
Uploaded: 2020-11-11T14:00:00
Description: Watch this Scientific Journal Video about Quantifying Fibrillar Collagen Organization with Curvelet Transform-Based Tools at JoVE.com

We thank many contributors and users to CT-FIRE and CurveAlign over the years, including Dr. Rob Nowak, Dr. Carolyn Pehlke, Dr. Jeremy Bredfeldt, Guneet Mehta, Andrew Leicht, Dr. Adib Keikhosravi, Dr. Matt Conklin, Dr. Jayne Squirrell, Dr. Paolo Provenzano, Dr. Brenda Ogle, Dr. Patricia Keely, Dr. Joseph Szulczewski, Dr. Suzanne Ponik and additional technical contributions from Swati Anand and Curtis Rueden. This work was supported by funding from Semiconductor Research Corporation, Morgridge Institute for Research, and NIH grants R01CA199996, R01CA181385 and U54CA210190 to K.W.E.

NOTE: This protocol describes the use of CT-FIRE and CurveAlign for collagen quantification. These two tools have complementary, but different, main goals and are linked together to some extent. CT-FIRE can be launched from the CurveAlign interface to conduct most operations except for advanced post-processing and ROI analysis. For a full operation of CT-FIRE, it should be launched separately.
1. Image collection and image requirement
NOTE: The tool can process any im...

This protocol describes the use of CT-FIRE and CurveAlign for fibrillar collagen quantification and can be applied to any image with collagen fibers or other line-like or fiber-like elongated structures suitable for analysis by CT-FIRE or CurveAlign. For example, elastin or elastic fibers could be processed in a similar way on this platform. We have tested both tools on computationally generated synthetic fibers21. Depending on the application, users should choose the analysis mode that is most ap...

Fibrillar collagens are prominent, structural ECM components. Their organization changes impact tissue function and are likely associated with the progression of many diseases ranging from osteogenesis imperfecta1, cardiac dysfunction2, and wound healing3 to different types of cancer including breast4,5,6, ovarian7,8, kidney9, and pancreatic cancers10. Many established imaging modalities can be used to visualize fibrillar collagen such as second harmonic generation microscopy11, stains or dyes in conjunction with bright field or fluorescence microscopy or polarized light microscopy12, liquid crystal-based polarization microscopy (LC-PolScope)13, and electron microscopy14. As the importance of fibrillar collagen organization has become clearer, and the use of these methods has increased, the need for improved collagen fiber analysis approaches has also grown.
There have been many efforts to develop computational methods for automated measurement of fibrillar collagen. Freely available software tools are mainly focused on the calculation of fiber alignment or orientation by adopting either first derivative or structure tensor for pixels15,16, or Fourier transform-based spectrum analysis for image tiles17. All these tools are subject to limitations such as the requirement of manual steps, inaccuracy in detection of the fiber edge in noisy background, or lack of localized feature characterization.
Compared to other freely available open-source free software tools, the methods described in this protocol use CT&#8212;an optimal, multiscale, directional image representation method&#8212;to remove noise from fibrillar collagen images and enhance or track fiber edges. Information about location and orientation can be provided directly from a fiber rather than by using the indirect pixel-wise or window-wise information to infer the metrics of fiber organization. This CT-based framework18,19,20,21 can quantify fiber organization on a global, ROI, or fiber basis, mainly via two separate, but linked, packages named &#8220;CT-FIRE&#8221;18,21 and &#8220;CurveAlign&#8221;19,21. As far as the implementation of the software is concerned, in CT-FIRE, CT coefficients on multiple scales can be used to reconstruct an image that enhances edges and reduces noise. Then, an individual fiber extraction algorithm is applied to the CT-reconstructed image to track fibers for finding their representative center points, extending fiber branches from the center points, and linking fiber branches to form a fiber network. In CurveAlign, CT coefficients on a user-specified scale can be used to track local fiber orientation, where the orientation and locations of curvelets are extracted and grouped to estimate the fiber orientation at the corresponding locations. This resulting quantification framework has been developed for more than ten years and has evolved greatly in many aspects such as functionality, user interface, and modularity. For instance, this tool can visualize local fiber orientation and allows the user to measure up to thirty fiber features including individual fiber properties such as length, angle, width, and straightness, as well as bulk measurements such as density and alignment. Additionally, the user can measure fiber angle relative to manually or automatically segmented boundaries, which, for example, plays an important role in image-based biomarker development in breast cancer22 and pancreatic cancer studies10. This platform provides several feature modules including ones for ROI analysis, automatic boundary creation, and post-processing. The ROI module can be used to annotate different shapes of ROI and conduct corresponding&#160;ROI analysis. As an application example, the automatic boundary creation module can be used to register hematoxylin and eosin (H&#38;E) bright field images with second harmonic generation (SHG) images and generate the image mask of tumor boundaries from the registered H&#38;E images. The post-processing module can help facilitate the processing and integration of output data files from individual images for possible statistical analysis.
This quantification platform does not require prior experience of programming or image processing and can handle large datasets including hundreds or thousands of images, enabling efficient quantification of collagen organization for biological or biomedical applications. It has been widely used in different research fields by many researchers all over the world, including ourselves. There are four main publications on CT-FIRE and CurveAlign18,19,20,21, out of which the first three have been cited 272 times (as of 2020-05-04 according to Google Scholar). A review of the publications that cited this platform (CT-FIRE or CurveAlign) indicates that there are approximately 110 journal papers that directly used it for their analysis, in which approximately 35 publications were collaborative with our group, and the others (~ 75) were written by other groups. For instance, this platform was used for the following studies: breast cancer22,23,24, pancreatic cancer10,25, kidney cancer9,26, wound healing3,27,28,29,30, ovarian cancer8,31,7, uterosacral ligament32, hypophosphatemic dentin33, basal cell carcinoma34, hypoxic sarcoma35, cartilage tissue36, cardiac dysfunction37, neurons38, glioblastoma39, lymphatic contractions40, fibrous cacffolds41, gastric cancer42, microtubule43, and bladder fibrosis44. Figure 1 demonstrates the cancer imaging application of CurveAlign to find the tumor-associated collagen signatures of breast cancer19 from the SHG image. Figure 2 describes a typical schematic workflow of this platform. Although these tools have been reviewed technically18,19,21, and a regular protocol20 for alignment analysis with CurveAlign is also available, a visual protocol that demonstrates all the essential features could be useful. A visualized protocol, as presented here, will facilitate the learning process of using this platform as well as more efficiently address concerns and questions that users might have.

<table>
	<tbody>
		<tr>
			<td>CT-FIRE</td>
			<td>Univerity of Wisconsin-Madison</td>
			<td>N/A</td>
			<td>open source software available from https://eliceirilab.org/software/ctfire/</td>
		</tr>
		<tr>
			<td>CurveAlign</td>
			<td>University of Wisconsin-Madison</td>
			<td>N/A</td>
			<td>open source software available from https://eliceirilab.org/software/curvealign/</td>
		</tr>
	</tbody>
</table>

quantifying fibrillar collagen organization with curvelet transform-based tools

Fibrillar collagens are prominent extracellular matrix (ECM) components, and their topology changes have been shown to be associated with the progression of a wide range of diseases including breast, ovarian, kidney, and pancreatic cancers. Freely available fiber quantification software tools are mainly focused on the calculation of fiber alignment or orientation, and they are subject to limitations such as the requirement of manual steps, inaccuracy in detection of the fiber edge in noisy background, or lack of localized feature characterization. The collagen fiber quantitation tool described in this protocol is characterized by using an optimal multiscale image representation enabled by curvelet transform (CT). This algorithmic approach allows for the removal of noise from fibrillar collagen images and the enhancement of fiber edges to provide location and orientation information directly from a fiber, rather than using the indirect pixel-wise or window-wise information obtained from other tools. This CT-based framework contains two separate, but linked, packages named &#8220;CT-FIRE&#8221; and &#8220;CurveAlign&#8221; that can quantify fiber organization on a global, region of interest (ROI), or individual fiber basis. This quantification framework has been developed for more than ten years and has now evolved into a comprehensive and user-driven collagen quantification platform. Using this platform, one can measure up to about thirty fiber features including individual fiber properties such as length, angle, width, and straightness, as well as bulk measurements such as density and alignment. Additionally, the user can measure fiber angle relative to manually or automatically segmented boundaries. This platform also provides several additional modules including ones for ROI analysis, automatic boundary creation, and post-processing. Using this platform does not require prior experience of programming or image processing, and it can handle large datasets including hundreds or thousands of images, enabling efficient quantification of collagen fiber organization for biological or biomedical applications.

These methods have been successfully applied in numerous studies. Some typical applications include: 1) Conklin et al.22 used CurveAlign to calculate tumor-associated collagen signatures, and found that collagen fibers were more frequently aligned perpendicularly to the duct perimeter in ductal carcinoma in situ (DCIS) lesions; 2) Drifka et al.10 used the CT-FIRE mode in CurveAlign to quantify the stromal collagen alignment for pancreatic ductal adenocarcinoma and normal/ch...

Watch this Scientific Journal Video about Quantifying Fibrillar Collagen Organization with Curvelet Transform-Based Tools at JoVE.com

Quantifying Fibrillar Collagen Organization with Curvelet Transform-Based Tools

Here, we present a protocol to use a curvelet transform-based, open-source MATLAB software tool for quantifying fibrillar collagen organization in the extracellular matrix of both normal and diseased tissues. This tool can be applied to images with collagen fibers or other types of line-like structures.

Fibrillar collagens are prominent extracellular matrix (ECM) components, and their topology changes have been shown to be associated with the progression ...

This tool helps to quantify changes in fibrillar collagen organization in the extracellular matrix related to many diseases on a global region of interest and individual fiber basis. This tool uses the curvelet transform, an optimal multi-scale directional image representation method to remove image noises and to enhance fiber edges. Comprehensive and user-friendly modules enable an efficient quantification.For individual fiber extraction, follow the protocol to open the CT-FIRE and click open files to select one or more images of interest. In the parameters panel, click update and adjust the background threshold and the nucleation searching radius. When all of the parameters have been set, click run.After the analysis is done, click any element in the output table to visualize the results and check the ctFIREout subfolder for the output files. To analyze a region of interest, click open files to load one or more images and select the image to be annotated. In the run options panel, select region of interest manager in the dropdown menu and click run to launch the region of interest manager module.Next, click the draw region of interest menu D dropdown menu and draw the regions of interest. If full image CT-FIRE analysis has been conducted and the results are saved in the default directory, click one or more regions of interest in the region of interest list and click CT-FIRE region of interest analyzer to launch the post region of interest analysis module. If full image CT-FIRE analysis has not been conducted, click one or more of the regions of interest in the region of interest list and click apply CT-FIRE on ROI to directly apply the CT-FIRE analysis to the selected regions of interest.For advanced post-processing, check the out. adv checkbox and click post-processing to launch the advanced post-processing graphic interface. In the analysis module, click select file and visualize fibers to allow the fiber number to be entered based on the labels in the original fibers tab.Click confirm update to move to the thresholding operation and check the thresholding box to enable the threshold settings. Select one of the four thresholding options from the dropdown menu and enter the desired thresholds in the thresholds panel for one or more fiber properties. Then click threshold now to apply the above thresholding conditions and check the prompt figure with the name ending with metrics visualization to see the selected fibers overlaid on the original images with the customized color maps.When the threshold has been set, click save fibers to save the selected fiber information and click generate stats and click OK to generate the summary statistics. To combine results from multiple images, check the batch mode and select multiple images to be analyzed before generating the summary statistics as demonstrated. To extract CT-based fiber features, follow the protocol to open the CurveAlign software and click get images in the main graphic user interface and select one or more images or image stacks from the prompt window.Enter the fraction of coeffs to keep value, the fraction of the largest coefficients of CT that will be used in the fiber analysis, and click run. Repeat to set a desirable threshold. When the process is complete, some summary statistics for each image will be displayed in an output table and all of the output files will be automatically saved in a subfolder named CA_out in the directory of the original images.To extract CT-FIRE based fiber features, click the fiber analysis method dropdown menu and select CT-FIRE fibers to use the fiber center point and fiber angle to represent the fiber. For relative alignment analysis of the tumor boundary, select the TIFF boundary condition. Open the CurveAlign software and click get images in the main graphic user interface and select one or more images or image stacks from the prompt window.In the primary parameters panel, enter the distance from the closest boundary to only evaluate the fibers within this distance range. In the output options panel, check the boundary association box to allow visualization of the point on the boundary that is associated with a fiber, fiber segment, or curvelet and click run, then click the output table to visualize the results. If a full image CurveAlign analysis has been conducted and the results are saved in the default directory, click one or more regions of interest in the region of interest list and click CurveAlign region of interest analyzer to run a post region of interest analysis.If a full image CurveAlign analysis has not been conducted, click one or more regions of interest in the region of interest list and click apply CurveAlign on region of interest to directly apply the CurveAlign analysis to the selected regions of interest. In this image, an overlay image of a tissue microarray core can be observed. In the magnification, the fibers parallel and perpendicular to the tumor boundary within the region of interest can be observed.The CurveAlign output overlay image shows the tumor boundaries and the representative fiber locations and orientations. This method can be used to quantify not only collagen fibers, but also other line-like structures such as elastin and can potentially facilitate early diagnosis and prognostic analysis of cancer.

Research

JoVE Journal

Bioengineering

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