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Bioengineering

Quantifying Fibrillar Collagen Organization with Curvelet Transform-Based Tools

doi: 10.3791/61931 Published: November 11, 2020
Yuming Liu1, Kevin W. Eliceiri1,2,3

Abstract

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 “CT-FIRE” and “CurveAlign” 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.

Introduction

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—an optimal, multiscale, directional image representation method—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 “CT-FIRE”18,21 and “CurveAlign”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 ROI analysis. As an application example, the automatic boundary creation module can be used to register hematoxylin and eosin (H&E) bright field images with second harmonic generation (SHG) images and generate the image mask of tumor boundaries from the registered H&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.

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Protocol

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 image file with line-like structures readable by MATLAB regardless of the imaging modality used to collect it.

  1. Use 8-bit grayscale as the image type as the default running parameters are based on this format.
    NOTE: SHG imaging is a widely used label-free and high-resolution fibrillar collagen imaging method. SHG images from a breast cancer study19 will be used here for the purpose of demonstration.

2. Software installation and system requirement

NOTE: Both standalone and source-code versions are freely available. The source code version requires a full MATLAB installation including toolboxes of Signal Processing, Image Processing, Statistics Analysis, and Parallel Computing. To run the source-code version, all the necessary folders including some from the third-party sources should be added to the MATLAB path. Use of the standalone application (APP) is recommended for most users, which requires an installation of a freely available MATLAB Compiler Runtime (MCR) of specified version. The procedure of installing and launching the APP is described below.

  1. Download CT-FIRE version 3.0 (CTF3.0) and CurveAlign Version 5.0 (CA5.0) APP packages from https://eliceirilab.org/software/ctfire/ and https://eliceirilab.org/software/curvealign/, respectively.
    NOTE: Each package includes the standalone APP, manual, and test images.
  2. Follow the detailed requirements and installation instructions from the above websites to install MATLAB MCR 2018b.
  3. Launch the APP.
    1. For a Windows 64-bit system, double click on the APP icon to launch it.
    2. For a Mac system, follow the following steps to launch it: Right click on the APP (ctrl-click) | Show Package Contents | Contents | MacOS | applauncher (right-click and choose open).
      NOTE: Other details can be seen in the software websites listed in 2.1.

3. Individual fiber extraction with CT-FIRE

NOTE: CT-FIRE uses CT to denoise the image, enhance the fiber edges, and then uses a fiber extraction algorithm to track individual fibers. Length, angle, width, and straightness are calculated for individual fibers.

  1. CT-FIRE on single image or multiple images
    1. Launch the APP as described in 2.3.
    2. Click on the Open File(s) button in the main graphical user interface (GUI, Figure 3A), and then select one or more images/or image stacks from the prompt window. Use the technique appropriate for the operating system to select multiple images in the dialog (e.g., in Windows, hold CTRL while selecting multiple files).
      NOTE: If two or more image files are selected, all the images must use the same running parameters for the analysis. Make sure that all the images are acquired under the same or similar conditions.
    3. Select parallel computing options by checking the Parallel checkbox on the top right corner for multiple images analysis.
    4. For the image stack, move the slice slider under the file listbox to select the slice to be analyzed.
    5. Set running properties. Use default parameters for an initial analysis of some images. If using default parameters, skip to step 3.1.6. To set different parameter(s), click on the Update button in the Parameters panel. Follow the manual to properly tune the parameters.
      NOTE: The most frequently adjusted parameters include the background threshold (thresh_im2) and the nucleation searching radius (s_xlinkbox). If the background noise level is high, set thresh_im2 to a larger value; s_xlinkbox is associated with the average radius of the fibers, set a smaller value to detect thin fibers.
    6. Click on the Run button.
      NOTE: The progress information will be displayed in both the information window and the command window. After the analysis is complete, the output table will be displayed (Figure 3B).
    7. Click on any item in the output table to see the histogram of fiber measures (Figure 3C and Figure 3F) of the image including length, width, angle, and straightness.
      NOTE: The fiber images with fibers overlaid on the original image will also be displayed (Figure 3E).
    8. Check the subfolder named ctFIREout under the image folder for the output files including the overlaid image “.tiff” file, “.csv” file, and “.mat” file.
  2. CT-FIRE region of interest (ROI) analysis
    1. ROI annotation using ROI Manager
      1. Click on the Open File(s) button in the main GUI (Figure 3A) to load one or more images.
      2. Select the image to be annotated in the file list.
      3. Select the ROI Manager in the dropdown menu of ROI Options panel.
      4. Click on the RUN button to launch the ROI Manager module (Figure 3A).
      5. In the ROI manager GUI (Figure 4A), click on the dropdown menu below Draw ROI Menu(d) to draw the ROIs, one by one.
        NOTE: The ROI shape can be rectangle, freehand, ellipse, polygon, or specified rectangle. Follow the on-screen instructions to draw, save, and quit ROI annotation.
      6. After selecting the method to draw the ROI, drag the yellow rectangle that appears on the original image to the desired position, and then click on the Save ROI(s) button or press the key s to add this ROI to the ROI list. This ROI will be automatically named.
      7. Draw a new ROI by dragging the previous ROI to a new position, and save it as mentioned in 3.2.1.6, or repeat steps 3.2.1.5–3.2.1.6 to draw a new ROI.
      8. Press x or select New ROI? in the ROI shape dropdown menu to quit ROI annotation.
      9. Check the checkboxes Show All and Labels to show all the defined ROIs on the list and their names on the original image.
      10. Select the ROI on the ROI list to conduct basic ROI operations including Rename ROI, Delete ROI, Save ROI Text, Load ROI from Text, Save ROI Mask, Load ROI from Mask, and Combine ROIs.
      11. Check the output file of the ROI manager saved as “.mat” file in a subfolder named ROI_management under the original image folder.
      12. To annotate another image in the opened file list, repeat steps 3.2.1.2–3.2.1.11.
      13. After the annotation is done, close the ROI manager GUI, and reset the main GUI by clicking on the Reset button in the main GUI.
    2. ROI analysis for a single image in ROI Manager
      1. If full-image CT-FIRE analysis is conducted and the results are saved in the default directory, click on one or more ROIs in the ROI list, then click on the ctFIRE ROI Analyzer button to launch the Post-ROI analysis module.
        NOTE: The results will be automatically saved in a subfolder located at \\[image folder]\ CTF_ROI\Individual\ROI_post_analysis\.
      2. In the pop-up window, click on the Check Fibers button to display fibers within the selected ROIs (Figure 4B).
      3. Click on Plot Statistics to display histograms of each ROI (Figure 4C). The corresponding output figures will be displayed.
      4. If full image CT-FIRE analysis has not been conducted, click on one or more ROIs in the ROI list, and click on the Apply ctFIRE on ROI button to directly apply CT-FIRE analysis to the selected ROIs.
      5. Follow the instructions in the prompt window to run the analysis.
        NOTE: The parameters for running the CT-FIRE are passed through the main GUI, and the user can update the running parameters as described in step 3.1.5 as needed. After the analysis is complete, the summary statistics of fiber measures will be displayed in the output table. The results will be automatically saved in a subfolder located at \\[image folder]\ CTF_ROI\Individual\ROI_analysis\.
    3. ROI analysis for multiple images using ROI Analyzer
      1. Follow the steps in 3.2.1 to annotate ROIs for the images to be analyzed.
      2. Open one or more images by clicking on the Open File(s) button.
      3. To run ROI post analysis when the full image analysis results are available, click on the dropdown menu in the Run Options panel and select the option CTF post-ROI analyzer.
      4. Click on the RUN button to run ROI analysis for all the loaded images.
      5. Check the progress information displayed in the message window at the bottom of the GUI and in the command window.
      6. After the analysis is complete, check the summary statistics for each ROI displayed in an output table.
        NOTE: The detailed output files are automatically saved in a subfolder located at \\ [image folder]\CTF_ROI\Batch\ROI_post_analysis\.
      7. To run a direct analysis when the full image analysis results are not available, follow steps 3.2.3.1–3.2.3.6, except that in step 3.2.3.3, select the option CTF ROI analyzer; in step 3.2.3.4, before clicking on the RUN button, update the running parameters as described in step 3.1.5. After clicking on the RUN button, in a prompt dialog window, choose between Rectangular ROI and ROI mask of any shape.
        NOTE: If all the annotated ROIs are rectangular, the user can choose the “Rectangular ROI”. In step 3.2.3.6, the ROI analysis results are saved in a subfolder located at \\[image folder]\CTF_ROI\Batch\ROI_analysis\.
  3. Post-processing with CT-FIRE
    NOTE: After the regular CT-FIRE analysis described in 3.1, the user can perform further post-processing. Without running the time-consuming fiber extraction again, regular post-processing, described in 3.3.1, can update some basic output figure properties, whereas the advanced post-processing described in 3.3.2 can visualize individual fibers and their properties, perform complex thresholding among all the four fiber properties, generate summary statistics of the selected fibers, and visualize the selected fibers using a customized color map.
    1. Regular post-processing with CT-FIRE
      1. Launch the CT-FIRE app, or click on the Reset button after other operations to initialize the CT-FIRE main GUI (Figure 3A).
      2. Check the .mat checkbox on the top of the main GUI.
      3. Click on the Open File(s) button to select the CT-FIRE output .mat file in the ctFIREout subfolder.
        NOTE: If multiple files are selected, the Batch checkbox will be checked automatically. The file name of the corresponding images will be displayed in the box list.
      4. Update the options in the Output Figure Control panel.
      5. Keep the default options in Output Options, which will make sure all the output files will be updated according to the new set of parameters set in 3.3.1.4.
      6. Click on the Post-processing button. Check the progress information in the message window at the bottom of the main GUI as well as in the command window.
      7. After the analysis is complete, click on any item in the output table to see the histogram of fiber measures of the image including length, width, angle, and straightness.
        NOTE: New output files will overwrite the old ones in the ctFIREout subfolder.
    2. Advanced post-processing of CT-FIRE
      1. Launch the CT-FIRE app, or click on the Reset button after other operations to initialize the CT-FIRE main GUI (Figure 3A).
      2. Check the OUT.adv checkbox at the top of the main GUI (Figure 3A).
      3. Click on the Post-processing button to launch the advanced post-processing GUI named “Analysis Module” (Figure 5A).
      4. Click on the Select File button to select an image.
      5. Click on the Visualise Fibers button to enter fiber number based on the labels in the Tab figure Original-fibers.
        NOTE: The measurements of the selected fibers will be displayed in an output table (Figure 5B), and the corresponding fibers will be overlaid on the original image shown in the tab figure named Measured-Fibers (Figure 5C).
      6. Click on the Confirm/Update button to move to the thresholding operation.
      7. Check the thresholding box to enable the threshold settings.
      8. Choose one of the four thresholding options from the dropdown menu.
      9. Enter the desired thresholds in the Thresholds panel for one or more fiber properties.
      10. Click on the Threshold Now button to apply the above thresholding conditions.
      11. Check the prompt figure with its name ending with metrics visualization to see the selected fibers overlaid on the original images with the customized color maps as shown in Figure 5E.
      12. Repeat steps 3.3.2.9–3.3.2.11 to set the desirable thresholds.
      13. Click on the Save Fibers button to save the selected fiber information.
        NOTE: Corresponding selected fibers will be displayed in the tab figure named After-Thresholding.
      14. Click on the Generate stats button, and then click on the OK button in the pop-up window to generate summary statistics.
        NOTE: An output table (Figure 5D) will show the mean value of the selected fibers. Other statistics of the selected fibers will be saved in an excel file whose location is displayed in the status window at the bottom of this GUI.
      15. To include the selected individual fiber information in the output file, check the Generate sheet for raw data box before clicking on the OK button.
      16. To combine results from multiple images, in step 3.3.2.4, check the Batch Mode box or Stack Mode and select the multiple images or stack(s) to be analyzed; skip steps 3.3.2.5–3.3.2.6. In steps 3.3.2.8–3.3.2.9, set thresholding conditions, but as the buttons threshold now and Save Fibers are disabled, skip steps 3.3.2.10–3.3.2.13; and lastly, follow the instructions in step 3.3.2.14 to generate summary statistics and individual fiber properties of the selected fibers.

4. Fiber analysis with CurveAlign

NOTE: CurveAlign was initially developed to automatically measure angles of fibers with respect to user-defined boundaries. The current version of CurveAlign can be used for bulk assessment of density- and alignment-based features in addition to the relative angle measurement by either loading the individual fiber information extracted by CT-FIRE or directly using the local orientation of the curvelets. CurveAlign calculates up to thirty features related to global or local features mainly including density and alignment as well as individual fiber properties when CT-FIRE is adopted as the fiber tracking method.

  1. Fiber analysis with curvelets
    1. Launch the APP as described in 2.3.
    2. Click on the Reset button to reset the APP to its initial status if other operations have been conducted.
    3. In the main GUI (Figure 6A), check the Fiber analysis method option to make sure CT is selected (default option).
      NOTE: In this mode, CT is performed on the image, and the orientation of each curvelet represents the direction of a fiber at the corresponding location.
    4. Click on the Boundary method dropdown menu, and select the boundary processing mode from the following dropdown menu options: No Boundary, CSV Boundary, and TIFF Boundary.
      NOTE: If no boundary is needed, skip this step. Refer to 4.3 for how to calculate fiber angles with respect to a boundary.
    5. Click on the Get Image(s) button in the main GUI (Figure 6A), and then select one or more images/or image stacks from the prompt window. Use the technique appropriate for your operating system to select multiple images in the dialog (e.g., in Windows, hold CTRL while selecting multiple files).
      NOTE: If two or more image files are selected, all the images must use the same running parameters for analysis. Make sure that all the images are acquired under the same or similar conditions.
    6. For the image stack, move the slice slider under the file listbox to select the slice to be analyzed.
    7. Enter the Fraction of coefs to keep. This value is the fraction of the largest coefficients of CT that will be used in the fiber analysis.
      NOTE: If the image has a large variation in fiber intensity or contrast, annotate regions of interest with even contrast for the fiber analysis as this mode only detects the brightest fibers in an image. In addition, the larger the image size is, set a smaller value for this fraction.
    8. Keep all the parameters in the Output Options and others in the Advanced option as the default; the output files may be needed in other future operations.
    9. Click on the Run button at the bottom of the main GUI (Figure 6A).
      NOTE: The progress information will be displayed in a message window highlighted in green color at the bottom. After the process is done, some summary statistics for each image will be displayed in the output table (Figure 6B), and all the output files will be automatically saved in a sub-folder named CA_Out in the directory of the original image(s).
    10. Click on any item in the output table (Figure 6B) to see the histogram (Figure 6E) or compass plot (Figure 6F) of fiber angles.
      NOTE: The overlay image (Figure 6C) and heatmap (Figure 6D) of alignment or angle will also be displayed.
    11. Click on the Reset button to run other operations, or close the main GUI to quit the APP.
  2. Individual fiber analysis with CT-FIRE
    NOTE: The procedure is the same as the one described in section 4.1 except that in step 4.1.3, select CT-FIRE related fiber analysis mode, and skip step 4.1.7 as it is not applicable and is disabled in CT-FIRE mode. Specifically, in step 4.1.3, select one among the following three CT-FIRE-based individual fiber analysis methods:
    1. Select CT-FIRE Fibers to use the fiber center point and fiber angle to represent the fiber.
      NOTE: This option does not consider the changes in fiber orientation along the length of the fiber.
    2. Select CT-FIRE Endpoints to use the two endpoints of a fiber and corresponding fiber angle to represent the fiber.
      NOTE: Compared to 4.2.1, this option uses two positions to represent a fiber rather than one (center point of the fiber).
    3. Select CT-FIRE Segments to use segments of a fiber to represent the fiber.
      NOTE: Each segment has an equal length (set to 5 pixels by default in CT-FIRE) as well as its orientation and location, which reflects the change in orientation along the whole length of the fiber. This option would be the most time-consuming, but would be the best option among the three CT-FIRE-based fiber analysis methods to track changes in the local orientation of a curvy fiber.
  3. Relative alignment analysis with boundary
    NOTE: Compared to the regular analysis without boundary conditions described in sections 4.2 and 4.3, relative alignment analysis with boundary conditions needs the following:
    1. In step 4.1.3, select the tiff boundary condition.
      NOTE: The user will need a corresponding boundary file for each image or each stack. Follow the on-screen instructions to manually annotate either a CSV (comma-separated-values format based, x-y coordinates) boundary file or a Tiff boundary file. The boundary files created in CurveAlign will be automatically saved according to the file directory and file naming conventions described in the manual. If a pair of H&E bright-field and SHG images are provided, use the automatic boundary creation module described in section 4.4 to generate the boundary file.
    2. In the Primary Parameters panel, enter the distance from the closest boundary to only evaluate the fibers within this distance range.
    3. In the Output Options panel, check the boundary association box Bdry Assoc to visualize the point on the boundary that is associated with a fiber, fiber segment, or curvelet.
  4. Automatic boundary creation
    1. Launch the APP as described in 2.3.
    2. Click on the Reset button to reset the APP to its initial status if other operations have already been conducted.
    3. Click on the BD Creation button to launch the automatic boundary creation module.
    4. Follow the on-screen instructions/clues to create boundary file for one or more images based on a pair of H&E bright-field and SHG images.
    5. Close the module window, or click on the Reset button in the main GUI (Figure 6A) to quit this module.
  5. CurveAlign region of interest analysis
    1. ROI annotation using ROI Manager
      1. Click on the Get Image(s) button in the main GUI (Figure 6A) to load one or more images.
      2. Select the image to be annotated in the file list.
      3. Click on the ROI Manager to launch the ROI Manager module (Figure 7A).
      4. Follow steps 3.2.1.5–3.2.1.13 in section 3.2.1.
    2. ROI analysis for a single image in ROI Manager
      1. If full image CurveAlign analysis has been conducted, and the results are saved in the default directory, click on one or more ROIs in the ROI list, and then click on the CA ROI Analyzer button to run Post-ROI analysis.
        NOTE: After the analysis is complete, summary statistics will be displayed in an output table (Figure 7C) as well as a histogram figure (Figure 7D) showing the angle distribution.
      2. Click on any item in the output table to visualize the fibers in a given ROI (Figure 7B) as well as the histogram of the fiber angles.
      3. Check the output files saved in a subfolder located at \\[image folder]\ CA_ROI\Individual\ROI_post_analysis\.
      4. If full-image CA analysis has not been conducted, click on one or more ROIs in the ROI list, and click on the Apply CA on ROI button to directly apply CA analysis to the selected ROIs. Follow the instructions in the prompt window to run the analysis.
        NOTE: The parameters for running the CA analysis are passed through the main GUI; update the running parameters described in step 4.1.7 as needed. After the analysis is complete, the results of the summary statistics of fiber measures will be displayed in the output table. The results will be automatically saved in a subfolder located at \\[image folder]\ CA_ROI\Individual\ROI_analysis\.
    3. ROI analysis for multiple images using ROI Analyzer
      1. Follow the steps in 4.5.1 to annotate ROIs for the images to be analyzed.
      2. Open one or more images by clicking on the Get Image(s) button.
      3. To run ROI post analysis when the full image analysis results are available, click on the ROI Analysis button and select the option ROI post-processing.
      4. Check the progress information displayed in the message window at the bottom of the GUI and in the command window.
      5. After the analysis is complete, check the summary statistics for each ROI displayed in an output table.
        NOTE: The detailed output files are automatically saved in a subfolder located at \\ [image folder]\CA_ROI\Batch\ROI_post_analysis\.
      6. To run a direct analysis with CT-mode when the full image analysis results are not available, follow steps 4.5.3.1–4.5.3.5, except for the following changes: modify step 4.5.3.3 by selecting the option CA on cropped rectangular ROI or CA on mask with ROI of any shape. If all the annotated ROIs are rectangular shape, choose the Rectangular ROI option. After step 4.5.3.2, update the running parameters, as described in 4.1.7.
        NOTE: The ROI analysis results will be saved in a subfolder located at \\ [image folder]\CA_ROI\Batch\ROI_analysis\.
  6. Post-processing of CurveAlign
    1. Launch the APP as described in section 2.3.
    2. Click on the Reset button to reset the APP to its initial status if other operations have already been performed.
    3. Click on the Post-Processing button to launch the post-processing module.
    4. Follow the on-screen instructions/clues to combine the output features or values from different images.
    5. Close the module window, or click on the Reset button in the main GUI (Figure 6A) to quit this module.

5. Estimated running time

  1. Wait the estimated running time for processing an image with size of 1024 pixels x 1024 pixels with moderate fiber density. The actual computational time generally depends on multiple factors including the size of the file, the analysis mode, the features to be deployed, the central processing unit (CPU) type, and the amount of available random-access memory (or RAM). CT-FIRE individual fiber extraction takes a couple of minutes. CurveAlign CT-mode without boundary takes a few seconds. CurveAlign CT-FIRE fiber or fiber ends mode without boundary takes tens of seconds. CurveAlign CT-FIRE fiber mode without boundary takes hundreds of seconds. CurveAlign analysis with boundary takes tens of seconds to several minutes, depending on the complexity of the boundaries.

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Representative Results

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/chronic pancreatitis tissues, and found that there was an increased alignment in cancer tissues compared to that in normal/chronic tissues; 3) Alkmin et al.7 used CurveAlign to quantify the angular distribution of F-actin fibers and overall collagen alignment from the SHG images of ovarian stromal collagen, and showed that matrix morphology plays an important role in driving cell motility and F-actin alignment; 4) LeBert et al.3 applied CT-FIRE to the SHG images of a zebrafish wound repair model and found an increase in thickness of collagen fibers after acute wounding; 5) Devine et al.45 used the CT-FIRE mode in CurveAlign for SHG images of vocal fold collagen from different animal models to measure individual fiber properties and overall alignment, and showed that porcine and canine vocal fold collagen had a higher alignment and straightness inferiorly; 6) Keikhosravi et al.13 used CurveAlign to quantify collagen alignment in histopathology samples imaged with LC-PolScope, and showed that LC-PolScope and SHG are comparable in terms of alignment and orientation measurement for some types of tissue.

Figure 1
Figure 1: Using CurveAlign to find tumor-associated collagen signatures from SHG images of a human breast cancer tissue microarray (TMA). (A) Overlay image of a TMA core with SHG image (yellow) overlaid on the corresponding H&E bright field image. (B) The region of interest of (A). (C) The bright field image of (B). (D) The SHG image of (B). (F) The mask associated with the bright field image (C). (E) The CurveAlign output overlay image showing the tumor boundaries (yellow) from (F), representative fiber locations, and orientation (green lines); the blue lines are used to associate fibers with their closest boundaries. The green arrows in (B) and (E) show the fibers parallel to the tumor boundary, while the red arrows there show the fibers perpendicular to the boundary. The scale bar in (A) equals 200 µm. Images in (B)–(F) are displayed in the same scale, and the representative scale bar in (D) equals 50 µm. Abbreviations: SHG = second harmonic generation; H&E = hematoxylin and eosin. Please click here to view a larger version of this figure.

Figure 2
Figure 2: Schematic workflow of quantification of a fibrillar collagen image. (A) SHG image to be analyzed by CT-FIRE and/or CurveAlign. (B) Overlay image output by CT-FIRE. (C) Mask boundary of (A) is an optional CurveAlign input. (D) Overlay image output by CurveAlign. The color lines in (B) represent the extracted fibers. In (D), the green lines indicate the locations and orientations of fibers that are outside the boundaries (yellow lines) and are within the specified distance from their closest boundaries, the red lines are those of other fibers, and the blue lines are used to associate fibers with their closest boundaries. Images in (A)–(D) are displayed in the same scale, and the representative scale bar in (A) equals 200 µm. Please click here to view a larger version of this figure.

Figure 3
Figure 3: CT-FIRE regular analysis. (A) Main GUI. (B) Output table showing the summary statistics. (C) and (F) show the histograms of angle and width, respectively. (E) Output image showing the extracted fibers (color lines) overlaid on the original SHG image (D). Abbreviations: GUI = CT = curvelet transform; graphical user interface; SHG = second harmonic generation. Please click here to view a larger version of this figure.

Figure 4
Figure 4: CT-FIRE ROI management module. (A) Module GUI. (B) ROI post-analysis of four ROIs with different shapes showing the fibers within each ROI. (C) ROI histograms of different fiber properties. Abbreviations: CT = curvelet transform; GUI = graphical user interface; ROI = region of interest. Please click here to view a larger version of this figure.

Figure 5
Figure 5: CT-FIRE advanced post-processing module. (A) Module GUI. (B) Measurements of selected three fibers. (C) Visualization of the selected three fibers in (B). (D) Summary statistics after applying a length threshold (>60 pixels). (E) Visualization of the fibers selected in (D) with length-based color bar. Abbreviations: CT = curvelet transform; GUI = graphical user interface. Please click here to view a larger version of this figure.

Figure 6
Figure 6: CurveAlign regular analysis. (A) Main GUI. (B) Output table showing the summary statistics. (C) Output image showing the locations and orientation of representative fibers (green lines) and boundaries (yellow lines) overlaid on the original SHG image, the blue lines are used to associate fibers with their closest boundaries, red lines show the locations and orientations of fibers inside a boundary or outside far away from the boundary (>user specified distance, e.g., 250 pixels here). (D) Heatmap of the angles: red (> 60 degrees), yellow (45–60] degrees, green (10, 45] degrees. (E)–(F) show the angle distribution using histogram and compass plot, respectively. Abbreviations: GUI = graphical user interface; SHG = second harmonic generation. Please click here to view a larger version of this figure.

Figure 7
Figure 7: CurveAlign ROI management module. (A) The module GUI. (B) Four annotated rectangular ROIs overlaid on the original image. (C) ROI post-analysis output table. (D) Angle histogram of each ROI. Abbreviations: ROI = region of interest; GUI = graphical user interface. Please click here to view a larger version of this figure.

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Discussion

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 appropriate for their data. The CT fiber analysis mode can directly use curvelets in CT to represent fiber location and orientation, and it is sensitive to changes in local fiber structure. The CT-mode can be used to locate fibers and their orientation in complex conditions, e.g., where the noise level is high, the fiber is curvy, or the variation in fiber thickness is high. However, as the CT-mode only picks up the brightest parts of an image, it would miss some fibers with lower intensity when there is a large variation in image intensity.

Moreover, the CT-mode does not provide information on individual fibers. In contrast to the CT-mode, the CT-FIRE mode calculates individual fiber properties and can analyze all the fibers whose intensity is above a specified threshold. The challenges associated with the CT-FIRE mode include: 1) the accuracy of an intact fiber extraction may be reduced or compromised when there is large variation in the intensity along a fiber or the fiber thickness across an image; and 2) the current standard analysis is computationally demanding as mentioned in the protocol. More details about the advantages and limitations of these methods can be seen in our previous publications20,21.

As far as the accuracy of fiber tracking is concerned, the user can mainly rely on visual inspection to check the overlapping image where the extracted fibers or representative orientations are overlaid on the original image. In addition, for CT-FIRE, the user may use the advanced post-processing module to identify the properties of selected individual fibers, and compare them to measurements by using other image analysis tools such as Fiji46. For CurveAlign, the user may compare the orientation or alignment results to those calculated by other tools such as OrientationJ16 and CytoSpectre17.

Among the features available for output by the platform, alignment-related features are most frequently used and are the most reliable and robust. To use individual fiber features, the user needs to confirm the extraction of representative fiber features. Of note, an intact fiber may be divided into several shorter segments in some circumstances, which the user should take into consideration when selecting the fiber analysis mode or conducting further statistical analysis. Even when the fiber length cannot be directly used as a comparable property, the orientation or width of fiber segments weighted against their lengths might still indicate useful information. As far as SHG imaging is concerned, numeric aperture (NA) of the objective lens can significantly affect the detection of the width and length of a fiber, but it has less impact on the orientation and alignment measurements. In our experience in SHG imaging, we usually need to use objective lens with 40x magnification or higher with NA ≥ 1.0 to achieve a robust fiber thickness measurement.

“Alignment” can be interpreted in three different ways: 1) alignment with respect to the positive horizontal direction named “angle”, ranging from 0 to 180 degrees, where angles close to 0 have similar orientation to angles close to 180 degrees; 2) alignment with respect to a boundary named “relative angle”, ranging from 0 to 90 degrees, with 0 degrees indicating a fiber parallel to the boundary and 90 indicating a perpendicular fiber; and 3) alignment of fibers with respect to each other named “alignment coefficient”, ranging from 0 to 1, with 1 indicating perfectly aligned fibers and smaller values representing more randomly distributed fibers.

Besides the fiber features calculated in this platform, some metrics based on texture analysis47,48,49 were also proposed to quantify ECM patterns. Those texture-related features can provide an alternative or additional descriptor of the ECM in some applications. The challenges for the development of this type of metrics lie in the interoperation of the possible biological relevance, localized characterization, and the accuracy of tracing individual fibers.

To optimize the running parameters and perform troubleshooting, the user can refer to the manual, relevant publications20,21 as well as the FAQ sidebars on the GitHub Wiki pages of the curvelets repository: https://github.com/uw-loci/curvelets/wiki. For some buttons, a function hint may appear to guide the user for the current or next operation when the user moves the mouse icon above a button. Follow the information on the GUI or command window to perform the troubleshooting.

To process a large dataset, the user is encouraged to use parallel computing options, which enable the tool to process multiple images simultaneously. One option is using multiple CPU cores if available on the computer being utilized. Alternatively, a headless version of both APPs is provided and has been successfully compiled in the compilation node through the server held at the CONDOR-based50 Center for High Throughput Computing (CHTC) at the University of Wisconsin-Madison. The CHTC workflow for large scale fiber quantification has been developed, tested, and used successfully on real image sets consisting of thousands of images. The user could adapt the headless MATLAB functions of both CT-FIRE and CurveAlign to run quantification on other cloud computing systems including commercial services such as those offered by Amazon, Google, and Microsoft.

The ongoing and future development directions include: 1) incorporation of deep learning neural network to extract or generate high-quality synthetic collagen fiber images and improve the robustness and accuracy of fiber tracing algorithm; 2) integration of all the modules into a comprehensive platform while optimizing the code and documentation following the best software engineering practices; 3) deployment of all the core features on a cloud computing platform; 4) enhancement of the workflow of fiber analysis using CHTC service; and 5) improvement of the functionality of the synthetic fiber generator.

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Disclosures

The authors have nothing to disclose.

Acknowledgments

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.

Materials

Name Company Catalog Number Comments
CT-FIRE Univerity of Wisconsin-Madison N/A open source software available from https://eliceirilab.org/software/ctfire/
CurveAlign University of Wisconsin-Madison N/A open source software available from https://eliceirilab.org/software/curvealign/

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Liu, Y., Eliceiri, K. W. Quantifying Fibrillar Collagen Organization with Curvelet Transform-Based Tools. J. Vis. Exp. (165), e61931, doi:10.3791/61931 (2020).More

Liu, Y., Eliceiri, K. W. Quantifying Fibrillar Collagen Organization with Curvelet Transform-Based Tools. J. Vis. Exp. (165), e61931, doi:10.3791/61931 (2020).

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