Your support allows us to continue funding research into the causes and management of scoliosis, to improve quality of life for patients
Detecting anatomical differences between groups of individuals from the geometric or volumetric information extracted from medical images could help with predicting disease progression in conditions such as scoliosis. Based on different theories, a variety of computational approaches have been proposed to solve problems of statistical shape analysis of various anatomical structures. According to the nature of the information extracted from 3-D medical images, current endeavours in this area can be broadly divided into two categories, namely volume-based and boundary-based methods. The purpose of this paper is to compare volume- and boundary-based methods by applying them in the statistical analysis of 2-D shapes. The test data are 2-D shapes of the corpus callosum (fibres that connect the left & rights sides of the brain) obtained in patients with a particular type of adolescent idiopathic scoliosis (AIS) called left-thoracic AIS. The morphometric abnormality in the corpus callosum cross-validated by these different methods has the potential value in the prognosis and curve prediction of left-thoracic AIS.
In summary, this paper attempts to compare the volume and boundary-based methods through their applications on a simple 2-D shape, namely that of the corpus callosum. These experimental results confirm the existence of the difference in the corpus callosum between left thoracic AIS and healthy participants. This difference could signify generalized right–left asymmetry manifested in the skeletal growth in patients with AIS. The research team are planning to carry out a larger-sample study, as well as a longitudinal MRI study of AIS to examine the correlation between corpus callosum development and changes in the scoliosis and body growth.
A comparison of morphometric techniques for studying the shape of the corpus callosum in adolescent idiopathic scoliosis
Defeng Wang, Lin Shi , Winnie C.W. Chu , Tomáš Paus, Jack C.Y. Cheng, Pheng Ann Heng
NeuroImage 45 (2009) 738–748
Follow us