Brain shape morphometry has emerged as a preferred tool to investigate the morphological changes of them with respect to pathological processes (e.g. neurodegenerative diseases and aging). Various methods have been proposed in this field, but it is still challenging in estimating accurate and smooth surface boundaries against rough boundaries and in achieving good anatomical correspondence between the individual models. In addition, the sensitivity and comparability of the geometric measures, which quantify the shape differences between subjects, need to be validated and guaranteed for the comparison of the individuals'shapes with anatomical knowledge.
The objective of this research is to develop computational models to address these issues in the brain shape morphometry. Our approach can be defined as a template-based shape modeling and measurement. For this objective, we develop deformable surface models using a “progressive surface deformation” to reconstruct the target shapes robustly while preserving the geometric details of initial models (template models) against size and shape variations across subjects. In addition, we proposed structural feature descriptors and anatomical landmarks, anatomically and geometrically defined on the template models, to investigate the shape characteristics and changes of the brain ventricles with explicit measurement basis on anatomical knowledge. The proposed feature descriptors and landmarks guarantee the comparability and consistency across subjects. More specific information can be found in relevant papers.
Currently, we validated the accuracy, robustness and clinical consistency of the proposed methods on the human data of hippocampus and brain ventricles, collected from the Lothian Birth Cohort 1936 (LBC 1936) study by collaborating with the Centre for Clinical Brain Sciences (University of Edinburgh).
Hippocampal shape modeling and analysis
Shape morphometry using the structural feature descriptors and anatomical landmarks of brain ventricles
|KAIST||University of Edinburgh|
|Name||Computer Graphics and Visualization Lab.||Brain Research Imaging Centre|
|Address||Department of Computer Science, KAIST, 291 Daehak-ro, Yuseong-gu, Daejeon, Republic of Korea||NeuroImaging, Division of Clinical Neurosciences, Western General Hospital, Crewe Road South, EH4 2XU, Edinburgh, UK|
This work was funded by the National Research Foundation of Korea (Grant no. 2012K2A1A2033133/no.2011-0009761), the Row Fogo Charitable Trust, the Scottish Imaging Network A Platform for Scientific Excellence and Age UK for the LBC1936 Study. We also thank the Lothian Birth Cohort 1936 Study Collaborative Group at The University of Edinburgh led by Profs. Ian J. Deary and Joanna M. Wardlaw, who provided the data used in this manuscript.