Construction and Application of a Probabilistic Atlas of 3D Landmark Points for Initialization of Hippocampus Mesh Models in Brain MR Images

Abstract

The magnetic resonance (MR) imaging has become an indispensable tool for diagnosis and study of various brain diseases. To perform an accurate diagnosis of a brain disease and monitor its evolution and treatment outcomes, a neuroradiologist often needs to measure the volume and assess the changes of shapes in specific brain structures along a series of MR images. In general, brain structures are manually delineated by a radiologist and, therefore, they highly dependend on the professional’s skills. In this study, we proposed the construction of a probabilistic atlas consisting of 3D landmark points automatically detected in a set of MR images. Also, we aimed at investigate its applicability to guide the initial positioning of mesh models based on the deformation of the hippocampus in brain MR images. The normalized Dice Similarity Coefficient (DSC) and the Hausdorff Average Distance (HAD) were used for the quantitative performance evaluation of the proposed method. The results showed that the average values obtained by our atlas-based landmark approach were significantly better (DSC = 0.74/0.70, HAD = 0.70/0.73, for left and right hippocampus, respectively) than our previous initial approaches, such as the template-based landmark (DSC = 0.65/0.61, HAD = 0.88/0.91) and the affine transformation (DSC = 0.58/0.53, HAD = 1.10/1.22).