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Reconstruction of the geometric central surface of the human cerebral cortex is an important means to study the structure and function of the brain cortex. In this paper, we propose a novel method based on an elastic deformable transform vector field to drive a deformable model for the reconstruction of the central surface of the brain cortex. In addition, simulated brain cortexes are generated to evaluate this method. We report the evaluation results obtained from ten subjects to show the effectiveness of our approach. We applied the central cortical surface reconstruction method and the hybrid cortical surface registration method to detect simulated brain atrophy. Our results indicate that the central cortical surface has much better sensitivity in detecting simulated atrophy than traditionally used inner or outer cortical surfaces

The development of automated and robust computational algorithms for 3D cell segmentation remains challenging in situations where the cells are touching each other or connected together. In this paper, we present a novel automated method that aims to tackle the aforementioned challenges in the segmentation of clustered or connected 3D cells. We first diffuse the gradient vector field with an elastic deformable transform. Then, a gradient flow tracking procedure is performed for each voxel to find the corresponding center that the point flows to. All points that flow to the same center are grouped as a region. In this way, the boundaries between touching cells are formed naturally, and the touched cells are divided. Evaluation results from 3D image data of zebrafish and C. Elegant indicate good performance of the proposed method