/UMIS

Official PyTorch implementation of "Unsupervised Microvascular Image Segmentation Using an Active Contours Mimicking Neural Network"

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UMIS

Official PyTorch implementation of "Unsupervised Microvascular Image Segmentation Using an Active Contours Mimicking Neural Network" (link)

Prerequisites

  • Python 3.6
  • Pytorch 0.4
  • Numpy
  • Scipy
  • OpenCV
  • Path
  • tqdm
  • h5py
  • tifffile
  • libtiff

Morphological Pooling Layer

In order to build the Morphological Pooling layer on your own machine, run the following line

python morphologicalpool/setup.py install

Train

You can now train using the Euler-Lagrange (original paper), or the PDE (level-set) loss with additional regularization for stability.

python train_unsup.py --loss <EL/LS>

Citation

@inproceedings{gur2019unsupervised,
  title={Unsupervised Microvascular Image Segmentation Using an Active Contours Mimicking Neural Network},
  author={Gur, Shir and Wolf, Lior and Golgher, Lior and Blinder, Pablo},
  booktitle={Proceedings of the IEEE International Conference on Computer Vision},
  pages={10722--10731},
  year={2019}
}