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QuickNATv2

Tool: QuickNAT: Segmenting MRI Neuroanatomy in 20 seconds

Authors: Abhijit Guha Roy, Sailesh Conjeti, Nassir Navab and Christian Wachinger

The code for training, as well as the Trained Models are provided here.

Deployment of existing off-the-shelf Model to segment any MRI scans is just by running RunFile.

Prerequisite:

1. An nvidia GPU with cuda compatibility
2. Installed Cuda Toolkit 8
3. CuDNN v5.1
4. Installed Matlab R2015a or later

Let us know if you face any problems running the code by posting in Issues.

If you use this code please cite:

Guha Roy, A., Conjeti, S., Navab, N., and Wachinger, C. 2018. QuickNAT: A Fully Convolutional Network for Quick and Accurate Segmentation of Neuroanatomy. Accepted for publication at NeuroImage, https://arxiv.org/abs/1801.04161.

Link to paper: Online demo: http://quicknat.ai-med.de

Enjoy!!! :)

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Implementations in Python

Tensorflow Implementation: https://github.com/IshmeetKaur/QuickNAT_tensorflow

PyTorch Implementation: https://github.com/abhi4ssj/quickNAT_pytorch

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Components and Steps to run

Deployment of pre-trained Model on Un-seen Data

1. sudo git-clone https://github.com/abhi4ssj/QuickNATv2/ (Download the repository)

2. Download MatConvNet (http://www.vlfeat.org/matconvnet/). Paste the extra layers from this repository to downloaded MatConvNet

'/MatConvNetLayers/DagNN_Layers/..' ---> 'MatConvNet/matlab/+dagnn/' and

'/MatConvNetLayers/SimpleNN_Layers/..' ---> 'MatConvNet/matlab/'

3. Compile the extracted MatConvNet (vlnncompile.m) with (enablegpu = true, enablecudnn = true) and providing the path of downloaded CuDNN. (Tested on Beta 24) (Refer: http://www.vlfeat.org/matconvnet/install/).

4. Go To folder '/RunFile/' of this repository and open the file RunFile.m

5. Enter the path and name of the MRI volume. (Make sure the Data has iso-tropic resolution of 256 x 256 x 256, use freesurfer to do it using the command: 'mri-convert --conform InputVol OutputVol'. It takes less than a second to do this.). The code uses the 'MRIread' routine from FreeSurfer for read/write operation. You may use your own customized version.

6. Run the Code and Get Segmentations saved in the same folder within 20secs!!! (Please note: 20sec is when deployed on Titan X Pascal 12GB GPU in Linux Ubuntu 16.04 OS. In the routine SegmentVol, the 'NumFrames' is passed as 70 to utilize this memory effectively. If you are using other GPU, please modify this to accomodate. 'NumFrames' of 10 works on a 4GB GTX 960M Laptop with segmentation time around 1 minute.)

7. Volume Estimation: Just count the number of voxels sum(Segmented_Output(:) == i), provides the volume of class ID 'i' in mm3. Refer the IDs below to find corresponding structure.

List of Classes with IDs

0 - 'Background'

1 - 'WM left'

2 - 'GM left'

3 - 'WM right'

4 - 'GM right'

5 - 'Ventricle left'

6 - 'Cerebellar WM left'

7 - 'Cerebellar GM left'

8 - 'Thalamus left'

9 - 'Caudate left'

10 -'Putamen left'

11 -'Pallidum left'

12 -'3rd ventricle'

13 -'4th ventricle'

14 -'Brainstem'

15 -'Hippo left'

16 -'Amygdala left'

17 -'VentralDC left'

18 -'Ventricle right'

19 -'Cerebellar WM right'

20 -'Cerebellar GM right'

21 -'Thalamus right'

22 -'Caudate right'

23 -'Putamen right'

24 -'Pallidum right'

25 -'Hippo right'

26 -'Amygdala right'

27 -'VentralDC right