Abstract
For many neuroscience applications, brain extraction in MRI images is the first pre-processing step of a quantification pipeline. Once the brain is extracted, further post-processing calculations become faster, more specific and easier to implement and interpret. It is the case, for example, of functional MRI brain studies, or relaxation time mappings and brain tissues classifications to characterise brain pathologies. Existing brain extraction tools are mostly adapted to work on the human anatomy, this gives poor results when applied to animal brain images. We have developed an atlas-based Veterinary Images Brain Extraction (VIBE) algorithm that encompasses a pre-processing step to adapt the atlas to the patient's image, and a subsequent registration step. We show that the brain extraction is achieved with excellent results in terms of Dice and Jaccard metrics. The algorithm is automatic, with no need to adapt the parameters in a broad range of situations: we successfully tested multiple MRI contrasts (T1-weighted, T2-weighted, T2-weighted FLAIR), all the acquisition planes (sagittal, dorsal, transverse), different animal species (dogs and cats) and canine cranial conformations (brachycephalic, mesocephalic, dolichocephalic). VIBE can be successfully extended to other animal species, provided that an atlas for that specific species exists. We show also how brain extraction, as a preliminary step, can help to segment brain tissues with a K-Means clustering algorithm.