Corpus Callosum 2D Segmentation on Diffusion Tensor Imaging Using Growing Neural Gas Network

TLDR: An automatic CC segmentation approach on Diffusion Tensor imaging (DTI) using Growing Neural Gas (GNG) network, an unsupervised machine learning algorithm, on the fractional anisotropy map is proposed.

Abstract: The Corpus Callosum (CC) segmentation on Magnetic Resonance Images (MRI) is of utmost importance for the study of neurodegenerative diseases, since it is the largest white matter brain structure, interconnecting the two cerebral hemispheres. Operator-independent segmentation methods are desirable, even though such task is complex due to shape and intensity variation among subjects, especially on low resolution images such as Diffusion-MRI. This paper proposes an automatic CC segmentation approach on Diffusion Tensor imaging (DTI). The method uses Growing Neural Gas (GNG) network, an unsupervised machine learning algorithm, on the fractional anisotropy map. The proposed method obtained a Dice coefficient of 0.88 in experiments using DTI of fifty human subjects, while other segmentation approaches obtained Dice results below 0.73. Although the GNG network had five parameters to be set, it requires no user intervention and was the only method that successfully detected and segmented the CC on all experimented dataset.