CCB Individual Researcher's Project - Zhuowen Tu

CCB40/LPBA40 56-ROI volume parser

• April 23, 2008 version of the brain parser for segmenting 56 structures in the LPBA40/CCB40 space is available at /cxfs/ccb/CCB_SW_Tools/VolumeTools/ZhuowenTu/deliverables/brain_parser/08_03_27/bin_linux/brain_parser_v2.0
• Corresponding trained model is at: /cxfs/ccb/CCB_SW_Tools/VolumeTools/ZhuowenTu/deliverables/brain_parser/models/LPBA40_56
• To test, run:
  • /cxfs/ccb/CCB_SW_Tools/VolumeTools/ZhuowenTu/deliverables/brain_parser/08_03_27/bin_linux/brain_parser_v2.0 “your_input.img” “your_label.img” –m /cxfs/ccb/CCB_SW_Tools/VolumeTools/ZhuowenTu/deliverables/brain_parser/models/LPBA40_56 –s 1.0
    • “-m” points to the model directory
    • “-s” controls the amount of smoothness you want.
• Prerequisites: The image to be segmented is supposed to be registered to the LPBA40 atlas (preferably using nonlinearly registeration to the atlas template /cxfs/ccb/CCB_SW_Tools/VolumeTools/ZhuowenTu/deliverables/brain_parser/models/LPBA40_56/appearance/0000.brain.img
• Notes:
  • It takes about half an hour to segment all the 56 structures.

• To train, you need to organize your data (in analyze image format) as the following:
  • Name all the MRI analyze images as “yourname.brain.hdr” and “yourname.brain.img” with the corresponding manual annotations as “yourname.label.hdr” and “yourname.label.img”.
  • Choose the best image in your training set and name it as “0000.brain.hdr” and “0000.brain.img” with the corresponding manual annotations as “0000.label.hdr” and “0000.label.img”. This will be your template image.
  • Copy all the images and their annotations into a directory.
  • Create a directory for saving the learned models.
  • run: cxfs/ccb/CCB_SW_Tools/VolumeTools/ZhuowenTu/deliverables/brain_parser/08_03_27/bin_linux/brain_parser_v2.0 -r 1 “your data directory” “your output directory” –n “number of structures”
    • “-r 1” refers to training
    • “-n” gives the number of anatomical structures you want the algorithm learn to segment
  • After the training is finished (ranging from several hours to a few days), Create a model directory with the naming convention as “your name/appearnce”
  • Copy files: setting.txt, atlas.3d,known.3d, label_mapping.txt, mri_intensity_common.3d,, mri_intensity_template.3dm to “your model directory/appearnce”
  • Copy clf files in each directory /1, /2/ 3 of the training result and rename the clf files start from /2 as clf, clf1, clf2. Copy them to “your model directory/appearnce”.
  • Now you are redy to test the model learned. Simply point the model directory to “your model directory”, not “your model directory/appearnce” though.
  • /cxfs/ccb/CCB_SW_Tools/VolumeTools/ZhuowenTu/deliverables/brain_parser/08_03_27/bin_linux/brain_parser_v2.0 “your_input.img” “your_label.img” –m “your model directory” –s 1.0

* reference for the paper describing the an early version of this method can be found at: Zhuowen Tu, Katherine Narr, Piotr Dollar, Iov Dinov, Paul Thompson, Arthur Toga, "Brain Anatomical Structure Segmentation by Hybrid Discriminative/Generative Models", IEEE Tran. on Medical Imaging, vol. 27, no. 4, pp. 495-508, April, 2008. The exact version is currently under review.

Cranium Grid Build

• ssh cerebro-rsn1.loni.ucla.edu
• cd /usr/local/loniApps/ztu

Automated Volume Parsing Mapping File