HyphaTracker - completed in GitHub Release!

https://github.com/evan-hammerstein/HyphaTracker-App/releases/tag/Release

This project is designed to analyze fungal growth using image processing techniques. It provides tools to process a sequence of tiff files representing a time-lapse of fungal growth, detect and track hyphal tips, calculate growth metrics, and visualize results. The analysis includes measurements of tip growth rates, branching frequency, spore identification, biomass quantification, and distances to regions of interest (ROIs).

Sample Images can be found here:

Features

1. Interactive GUI: File upload, metric specification, and output access and visualization

2. Image Preprocessing: Cropping, binarization, and skeletonization of grayscale images

3. Hyphal Analysis:

• Detect endpoints and calculate growth rates and angles

• Track hyphal tips and calculate distances to regions of interest (ROI)

• Analyze tip size and branching frequencies

4. Biomass Analysis: Measure fungal biomass over time

5. Spore Tracking:

• Identify spores based on size, shape, and proximity to biomass

• Track spores across multiple frames

6. Visualization: Generate visual outputs, including skeletonized images, tracked tips, and ROI distance visualizations

7. Metrics Output: Save results in CSV files and generate graphs for key metrics

Requirements

Software Dependencies (Check Releases for Packaged Version)

The code requires the following Python3 libraries:

• sys
• os
• cv2 (OpenCV)
• numpy
• skimage
• scipy
• matplotlib
• csv

Please ensure all dependencies are installed before running the script.

Input Data

• A folder containing grayscale image files (.tif format)

• Images should be named with frame numbers for sequential processing

Compatible Systems

• Mac OS

Usage Instructions

Step 1: Prepare Input Data

Place all .tif image files in a single folder

Ensure file names follow a pattern to allow frame numbers to be extracted (e.g., processed_frame_1.tif)

Step 2: Run the Script

Specify parameters like magnification, filter type, and sensitivity.

Upload the file folder for analysis and click 'Go'.

Step 3: Review Results

Results will be saved in the specified output folders:

1. CSV Files: Contain metrics for tips, spores, biomass, etc

2. Visualizations: Skeletonized images, distance from ROI and tracked tip visualizations

3. Graphs: Growth rates, branching frequencies, and other metrics as .png files

Key Functions

Image Preprocessing

preprocess_image: Crops and binarizes the image

skeletonize_image: Reduces the image structures to 1-pixel-wide lines

Hyphal Analysis

find_hyphal_endpoints: Detects hyphal tips/endpoints

track_tips_across_frames: Matches tips between frames

calculate_tip_size: Determines the size of a hyphal tip based on pixel area

track_tip_size_over_time: Tracks changes in hyphal tip size across multiple frames

calculate_overall_average_tip_size: Calculates the average size of all tips across frames

calculate_average_growth_rate: Computes the average growth rate of tips

calculate_growth_angles: Determines growth angles of tips relative to the horizontal

calculate_branching_rate: Identifies and counts overall branching events over time

Biomass Analysis

find_biomass: Calculates the area covered by fungal biomass

calculate_biomass_over_time: Tracks biomass change across frames

Spore Analysis

identify_spores: Detects spores based on size, shape, and proximity to hyphae

track_spores_over_time: Tracks spores across frames and calculates their size changes

Visualization

show_image: Displays an image with the option of saving it

display_tips: Visualizes the skeletonized image with identified tips marked

visualize_tracked_tips: Shows the tracked tips across frames

calculate_distances_to_roi_and_visualize: Computes and visualizes the distances of tips from a specified ROI

Additional Image Processing Functions

filter_hyphae: Removes small connected components (like noise or spores) to keep only large hyphae structures

process_frame: Applies normalization, thresholding, and morphological operations to enhance image quality for analysis

select_area: Allows user to interactively select an area for cropping

select_ROI: Enables selection of a rectangular region of interest (ROI)

Outputs

Outputs are organized into three folders; csv_files, graphs, and visuals. They are downloaded to the file explorer for access, and .tif files can be viewed on the platform.

CSV Files:

• Hyphal tip metrics (growth rate, angles, sizes)

• Branching frequency

• Biomass over time

• Spore number and size

Graphs:

Growth rates, branching frequencies, biomass trends, spore metrics

Visualizations:

• Skeletonized images with tips

• Tip distance from ROI visualizations

Customization

• Modify distance_threshold, min_size, or circularity_threshold to adjust sensitivity

• Update roi_polygon for custom regions of interest

• Change magnification to adjust pixel-to-area conversion factors

Notes

• Ensure input images have strong enough contrast

• Review logs for any warnings or errors

Contact

For questions or support, please contact evanhammerstein@gmail.com