Collagen fiber extraction and analysis in cancer tissue microenvironment

This repository is part of the paper

Collagen Fiber Centerline Tracking in Fibrotic Tissue via Deep Neural Networks with Variational Autoencoder-based Synthetic Training Data Generation,
Hyojoon Park*, Bin Li*, Yuming Liu, Michael S. Nelson, Helen M. Wilson, Eftychios Sifakis, Kevin W. Eliceiri,
Medical Image Analysis 2023.

Related repositories:

• DuoVAE

• Collagen fiber synthesis and extraction networks

Installation

Install anaconda/miniconda

  $ conda env create --name collagen --file env.yml
  $ conda activate collagen

If there are issues with OpenCV

  $ pip install opencv-contrib-python

Install ridge-detection package

  $ pip install ridge-detection

Install PyTorch

CenterLine class

This class handles the conversion between a centerline mask and a dictionary that contains the coordinates of individual centerlines, as well as the fiber property computation.
For example, create a CenterLine object using a binary centerline mask:

    from centerline import CenterLine
    centerline = CenterLine(centerline_image=io.imread("examples/example_centerline.png"), associate_image=io.imread("examples/example_image.tif"))

centerline_image is a binary centerline mask, associate_image is an optional image of collagen fiber to which the binary mask corresponds.
Compute the fiber properties:

    centerline.compute_fiber_feats() 
    print(dict(list(centerline.feats.items())[:-1]))

Create a colorized overlay of fiber centerline instances on the collagen fiber image:

    centerline_res.create_overlay()

Other ways to create a CenterLine object, check notebook centerline-basics.ipynb.

Read CT-FIRE results or use ridge detection

Check notebook centerline-baselines.ipynb

FiberExtractor class

This class handles the computation of a fiber centerline mask from a neural network.
Process a collagen fiber image:

    from fiber_extraction import FiberExtractor, UNet
    from skimage import io, img_as_uint
    net = UNet(1, 1, 16, True).eval()
    net.load_state_dict(torch.load('weights/netG.pt'))
    fiber_extractor = FiberExtractor(net)
    im_arr = img_as_uint(io.imread('examples/test_input.png'))
    result = fiber_extractor.compute(im_arr)

Compute the normalization range for 16-bit images. file_list is a list of directories to the image files.

    fiber_extractor.normalization_range(file_list=file_list)

This function computes the range in the 16-bit image set to be stretched to the range of (0, 65535).

Citation

@article{park2023collagen,
         title={Collagen fiber centerline tracking in fibrotic tissue via deep neural networks with variational autoencoder-based synthetic training data generation},
         author={Park, Hyojoon and Li, Bin and Liu, Yuming and Nelson, Michael S and Wilson, Helen M and Sifakis, Eftychios and Eliceiri, Kevin W},
         journal={Medical Image Analysis},
         volume={90},
         pages={102961},
         year={2023},
         publisher={Elsevier}
}