bhargavipatel / Unsuprevised_Seg_via_CNN

An unsupervised (or self-supervised) loss function for binary image segmentation.

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Unsupervised Segmentation Using Convolutional Neural Network

arXiv

This is a Tensorflow/Keras implementation of my paper:

Chen, Junyu, et al. "Medical Image Segmentation via Unsupervised Convolutional Neural Network. " Medical Imaging with Deep Learning (MIDL), 2020.

👋 Note that this method does binary segmentation. Please check out our new approach 👉 (FCM loss) for unsupervised and semi-supervised loss functions for multi-class segmentation (PyTorch and TensorFlow).

Network Architecture:

Evaluation and Results:

We evaluated four settings of the proposed algorithm on the task of bone segmentation in bone SPECT images:

  • Mode 1: Unsupervised (self-supervised) training with L_ACWE.

  • Mode 2: Mode 1 + fine-tuning using L_label with 10 ground truth (GT) labels.

  • Mode 3: Mode 1 + fine-tuning using L_label with 80 GT labels.

  • Mode 4: Training with L_ACWE + L_label.

The quantitative results can be found in the paper, and here are some qualitative results:

Comparing to traditional ACWE:

If you find this code is useful in your research, please consider to cite:

@inproceedings{
chen2020medical,
title={Medical Image Segmentation via Unsupervised Convolutional Neural Network},
author={Junyu Chen and Eric C. Frey},
booktitle={Medical Imaging with Deep Learning},
year={2020},
url={https://openreview.net/forum?id=XrbnSCv4LU}
}

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An unsupervised (or self-supervised) loss function for binary image segmentation.

License:MIT License


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