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C15 Standard brain template and multi-atlas based segmentation of tghd minipig brain
  1. Robin Schubert1,
  2. Frauke Freisfeld1,2,
  3. Hans Johnson3,
  4. Eunyoung Regina Kim3,
  5. Nina Nagelmann2,
  6. Jan Motlik4,
  7. Sarah Schramke1,5,
  8. Verena Schuldenzucker1,6,
  9. Lorena Rieke1,
  10. Tamara Matheis1,
  11. Maike Wirsig1,
  12. Cornelius Faber2,
  13. Ralf Reilmann1,2,7
  1. 1George-Huntington-Institute, Technology-Park, Muenster, Germany
  2. 2Department of Clinical Radiology, University of Muenster, Muenster, Germany
  3. 3Department of Psychiatry, University of Iowa, Iowa City, IA, USA
  4. 4Laboratory of Cell Regeneration and Plasticity, Institute of Animal Physiology and Genetics, Libechov, Czech Republic
  5. 5Institute for Animal Hygiene, Animal Welfare and Farm Animal Behaviour, University of Veterinary Medicine Hannover, Hannover, Germany
  6. 6Institute of Zoology, University of Veterinary Medicine Hannover, Hannover, Germany
  7. 7Department of Neurodegenerative Diseases and Hertie-Institute for Clinical Brain Research, University of Tuebingen, Tuebingen, Germany


Background In the TRACK tgHD minipig study, we aim to track longitudinal progression of the Libechov minipig, transgenic for the huntingtin gene. Annual MRI scans are conducted, including anatomical, spectroscopic and diffusion weighted imaging (DWI) sequences. Quantitative analyses require proper segmentation of the brain images. Standard Brain Templates are used as registration target, for intra subject comparison of various MR endpoints.

Aims Primary objective is to provide a multi-atlas for the Libechov minipig for automated segmentation of MR brain images. Secondary objectives are to achieve standard brain templates for various functions, including T1-weighted template and diffusivity templates.

Methods Nine T1 weighted, sagittal acquired MRI volumes of Libechov minipigs were transformed into a common coordinate system and manually skull-stripped. A domestic pig reference atlas was registered on the extracted brains with a landmark and intensity based algorithm. The BRAINSTools library was used for tissue segmentation and structural segmentation. The segmentation was cleaned up manually and merged with Multi-atlas joint label fusion.

The T1 weighted images were acquired with a 3 T Philips Achieva scanner.

Results A T1-weighted standard brain volume has been created, with 10 segmented regions of interest, covering the basal ganglia and surroundings.

Conclusions It was shown in previous data, that comprehensive MR assessments are feasible with the Libechov minipig, yielding high quality images. However, more work is needed to manually segment minipig brain images, to expand the multi-atlas to a feasible size. This will open the way to automated segmentation and thus to statistical analyses.

This study was supported by a grant of the CHDI Foundation.

  • Animal models
  • Brain atlas
  • MRI
  • Minipig
  • Preclinical research

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