Collagen fiber synthesis and extraction networks

PyTorch implementation of the collagen fiber centerline extraction network proposed in
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

• Collagen fiber extraction and analysis in cancer tissue microenvironment

• DuoVAE

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Installation

Train

Command format is python train.py <stage-number> --model-dir <model-directory>, for example

Stage I.

DuoVAE for generating collagen centerlines with desired centerline properties:

python train.py 1

Stage II.

cGAN for generating collagen images from collagen centerlines:

python train.py 2

Stage III.

UNet for extracting collagen centerlines from collagen images:

python train.py 3

The outputs will be saved in the directories output/stage1, output/stage2, and output/stage3.

Optional

To resume from a saved checkpoint, pass in --model-dir argument to a directory where the saved model (.pt files) is located and (optionally) set the number of starting epoch, for example

# resume from saved model in "output/stage1/model" at epoch 1000
python train.py 1 --model-dir output/stage1/model --starting-epoch 1000

Results

Stage 1 and 2

The figure shows representative outputs of the property-controlled data generation of DuoVAE on the collagen fiber dataset. The outputs are grouped into 6 panels according to the fiber properties:

• (a) orientation (from left-oriented to right-oriented),
• (b) alignment (from well-aligned to randomly organized),
• (c) density (from sparse to dense),
• (d) waviness (from straight to wavy),
• (e) average length (from short to long), and
• (f) length variation (from uniform lengths to random lengths).

The first rows in each panel are the fiber centerlines generated by DuoVAE, and the second rows are the corresponding collage image generated by the cGAN. The generated centerlines exhibit the different values of fiber properties specified in the generating process.

Stage 3

Representative results of input images captured using different objective magnifications (20× and 40×) are shown in the figure. The results show that the centerlines produced by the centerline extraction networks are consistently more similar to the ground truth annotations.

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}
}