Volume 4, Issue 1

Systematic Review of Computational Models for Human Brain Parcellation

Patrick Owate1, Benjamin Aribisala2, Charles Uwadia3, and Philip Adewole4
1Lagos State University, Ojo, Lagos State, Nigeria, Nigeria, 2Lagos State University, Ojo, Lagos State, Nigeria, Nigeria, 3University Of Lagos, Akoka, Lagos State, Nigeria, Nigeria, and 4University Of Lagos, Akoka, Lagos State, Nigeria, Nigeria


Introduction: The human brain consists of four main lobar sections: Frontal lobe, Parietal lobe, Temporal lobe and Occipital lobe. Most of the existing models used for the parcellation of brain into these lobes have limited accuracy when applied to ageing brain. Aim: To systematically review the existing models of parcellating brain Magnetic Resonance Images, their strengths and weaknesses, and the possibility of using them for ageing brain. Materials and Methods: PubMed was searched combining search terms for Parcellation, Brain and Magnetic Resonance Imaging (MRI). Articles were considered if they met the following criteria: Parcellation method was indicated, imaging technique was MRI, high resolution anatomical T1-Weighted was used, lobar regions were parcellated, number of lobar regions was indicated. Results: The search resulted into 569 articles. 174 articles (7 from the list of references) were potentially relevant and their abstracts were read. Out of these, 108 were not relevant because they either focused on animal studies, sub-cortical segmentation or tissue segmentation. The full papers of the remaining 66 were reviewed. 39 articles met the inclusion criteria. Various parcellation models were reviewed and summarized into six groups: supervised learning, unsupervised learning, region growing, shape and appearance, energy-based and atlas-based models. Conclusion: All the existing models identified were developed for parcellation of young adult brains and none of them used age-related information. Atlas-based model was found to perform the best among all the models. Future work should consider extending atlas-based model by including ageing information which could make them perform well on ageing brain.

Keywords: Parcellation, Human brain, Lobar regions, Models, Magnetic Resonance Imaging, MRI, and Ageing brain

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