Unet

Semantic Segmentation neural net based on Unet U-Net: Convolutional Networks for Biomedical Image Segmentation. Batch norms and dropouts are added to the network as well as weighted cross entropy loss for multi-class segmentation.

Dependencies

• python 2.7
• TensorFlow >=1.0.0
• In addition, please pip install -r requirements.txt to install the following packages:
  • Pillow
  • numpy
  • tensorflow>=1.0.0

Data Preprocessing

Tensor records are used as means of data storage for ease of use and distribution. To convert a normal dataset to tfrecords use data/dataset_to_tfrecords.py. The dataset images should be in the same folder (im1_color.png, im1_label.png) with PNG or JPG format. The label images must be 1 channel images.

Convert each dataset (training, testing) to tfrecords Datasets/tfrecords using

python data/dataset_to_tfrecords.py --data_dir Datasets/training --output_dir Datasets/tfrecords --name_color _image --name_label _label

name_color, name_label specifies the sffuix of the image presenting the RGB and labels respectively.

Training

• To train Unet run unet_train passing tfrecords dir

  python unet_train.py --tfrecords_dir ../Datasets/tfrecords  --checkpoint_dir ../Datasets/checkpoints
  

  Use help to check the other parameters

  python unet_train.py -h
  

Evaluation

• To evaluate Unet run unet_eval passing tfrecords dir

  python unet_eval.py --tfrecords_dir ../Datasets/tfrecords
  

  To save the predicted annotations as png files, pass in an output directory to the eval script

  python unet_eval.py  --tfrecords_dir ../Datasets/tfrecords --output_dir predictions
  

Citing Unet

Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 234–241. Springer (2015) pdf.

@inproceedings{fusenet2016accv,
 author    = "Olaf Ronneberger, Philipp Fischer, and Thomas Brox",
 title     = "U-Net: Convolutional Networks for Biomedical Image Segmentation",
 booktitle = "Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015",
 year      = "2015",
 month     = "October",
}