Cerebellum CNN

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Cerebellum Parcellation with Convolutional Neural Networks

Automatic cerebellum anatomical parcellation using U-Net with locally constrained optimization (ACAPULCO) is our current cerebellum parcellation method. The associated publication is:

• S. Han, A. Carass, Y. He, and J.L. Prince, "Automatic Cerebellum Anatomical Parcellation using U-Net with Locally Constrained Optimization", NeuroImage, 218:116819, 2020. (doi)

ACAPULCO
Docker image for Adult Data	1.2GB
Docker image for Pediatric Data	1.2GB

If you use it please cite the above paper. If you have any questions, please email Shuo Han at shan50 _AT_ jhu.edu.

Instructions

The instructions are available here.

Cerebellum labels

   "12":  "Corpus_Medullare",
   "33":  "Left_Lobules_I-III",
   "36":  "Right_Lobules_I-III",
   "43":  "Left_Lobule_IV",
   "46":  "Right_Lobule_IV",
   "53":  "Left_Lobule_V",
   "56":  "Right_Lobule_V",
   "63":  "Left_Lobule_VI",
   "60":  "Vermis_VI",
   "66":  "Right_Lobule_VI",
   "73":  "Left_Lobule_VIIAf",
   "76":  "Right_Lobule_VIIAf",
   "74":  "Left_Lobule_VIIAt",
   "70":  "Vermis_VII",
   "77":  "Right_Lobule_VIIAt",
   "75":  "Left_Lobule_VIIB",
   "78":  "Right_Lobule_VIIB",
   "83":  "Left_Lobule_VIIIA",
   "80":  "Vermis_VIII",
   "86":  "Right_Lobule_VIIIA",
   "84":  "Left_Lobule_VIIIB",
   "87":  "Right_Lobule_VIIIB",
   "93":  "Left_Lobule_IX",
   "90":  "Vermis_IX",
   "96":  "Right_Lobule_IX",
   "103": "Left_Lobule_X",
   "100": "Vermis_X",
   "106": "Right_Lobule_X"