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Image segmentation

1 U-Net: Image Segmentation using deep learning

Implement the U-net architecture for cell image data segmentation using PyTorch.

Figure 1: U-Net architecture[1]

The Convolutional Blocks Definition:

  1. a 3 × 3 un-padded convolution layer which takes a feature map with input_channel number of channels and outputs a feature map withoutput_channelnumber of channels
  2. a ReLU activation function
  3. another 3 × 3 un-padded convolution layer which keeps the number of channels unchanged
  4. a Batch normalization layer
  5. a ReLU activation function

Data augmentation:

Since the size of the data is too small for training a neural network with a huge number of parameters. Under this situation, the code has the following data augmentation applied:

  1. Horizontal/Vertical flip
  2. Zooming
  3. Rotation

Final Results:

Left is label, right is the corresponding prediction.

References

[1]Olaf Ronneberger, Philipp Fischer, and Thomas Brox. U-net: Convolutional networks for biomedical image segmentation. InInternational Conference on Medical image computing and computer-assisted intervention, pages 234–241. Springer, 2015.

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Implementation of UNet

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