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Protist-Image-Analysis

Code, data, and firgures for analyzing protistan parasites of diatoms via micrographs taken under fluorescent light with CalcoFluor White (CFW) staining

  • Problem (manual analysis): Too many images, not enough time

  • Problem (automated analysis): Complicated images characteristics, background noise, strange shapes, inconsistent cell boundaries, infection dynamics

  • Goal: Build a computationsl model that can accurately 1. Count cells, 2. Classify cell infection status by proportion of pixels in blue channel versus red channel

  • Aproach(es):

    • Normalize RGB color channels so that intensity ranges are comparable
      • Threshold channels
    • Mask individual color channels from thresholded images
      • Combine red and blue channels for a full cell mask
    • Model cells with Hough Line Transform (HLT)
    • Cluster pixels with K-means clustering
    • Apply Sobel edge detection to identify cell boundaries
    • Cluter pixels using Gaussian Mixture Models
    • Combin HLT & K-means methods

  • Model features:

    • Count individual cells
    • Calculate individual cell infected proportion via red & blue pixels within individual cell mask
      • Proportion outcomes determine infection classification

  • Eventual Outcomes:

    • Extrapolate population dynamics
      • Changes in infection ratios over time series facilitates inference about shifts in mixed-community population dynamics
  • Repository organization:
    • code:
      • benchmarking: "data_cleaning" includes code for cleaning raw csvs (result of running .ipynb files from ../code/final_models); model_evaluation.py evaluates performance using "./master_csvs/*.csv"; model_evaluation_statistics.csv is output from model_evaluation_F1.py
      • final_models: code for final versions of each 6 models
      • _deprecated: outdated code from various stages of model development
    • data:
      • test_images: set of ".jpg" files that models were trained on (note, models were only evaluated on AS*.csv files)
      • experimental_metadata.csv: metadata collected during lab experiment
      • model_evaluation_statistics.csv: output from "../code/benchmarking/model_evaluation_F1.py"
    • figures:
      • model_evaluation_statistics.csv: output from "../code/benchmarking/model_evaluation_F1.py"
      • video_explision.mp4: downloadable video of active parasitism
    • _deprecated:
      • outdated code and files from various stages of project development