Caffe with 3D CRF-RNN

This is a modified version of Caffe which supports the 3D Conditional Random Field Recurrent Neural Network (3D CRF-RNN) architecture as described in our paper Automatic bladder segmentation from CT images using deep CNN and 3D fully connected CRF-RNN. The implementation of 3D CRF-RNN is extended from the 2D CRF-RNN.

This code has been compiled and passed on Windows 7 (64 bits) using Visual Studio 2013.

How to build

Requirements: Visual Studio 2013, ITK-4.10, CUDA 8.0 and cuDNN v5

Pre-Build Steps

Please make sure CUDA and cuDNN have been installed correctly on your computer.

Clone the project by running:

git clone https://github.com/superxuang/caffe_3d_crf_rnn.git

In .\windows\Caffe.bat set ITK_PATH to ITK intall path (the path containing ITK include,lib folders).

Build

Run .\windows\Caffe.bat and build the project caffe in Visual Studio 2013.

License and Citation

Please cite our paper and Caffe if it is useful for your research:

@article{Xu_2018,
  author="Xu, Xuanang and Zhou, Fugen and Liu, Bo",
  title="Automatic bladder segmentation from CT images using deep CNN and 3D fully connected CRF-RNN",
  journal="International Journal of Computer Assisted Radiology and Surgery",
  year="2018",
  month="Jul",
  day="01",
  volume="13",
  number="7",
  pages="967--975",
  issn="1861-6429",
  doi="10.1007/s11548-018-1733-7",
  url="https://doi.org/10.1007/s11548-018-1733-7"
}

Caffe is released under the BSD 2-Clause license. The BVLC reference models are released for unrestricted use.

Please cite Caffe in your publications if it helps your research:

@article{jia2014caffe,
  Author = {Jia, Yangqing and Shelhamer, Evan and Donahue, Jeff and Karayev, Sergey and Long, Jonathan and Girshick, Ross and Guadarrama, Sergio and Darrell, Trevor},
  Journal = {arXiv preprint arXiv:1408.5093},
  Title = {Caffe: Convolutional Architecture for Fast Feature Embedding},
  Year = {2014}
}

Contact

You are welcome to contact us:

• xux12@rpi.edu (Dr. Xuanang Xu)