An Automatic Method for Nucleus Boundary Segmentation Based on a Closed Cubic Spline - Feng, Zhao; Li, Anan; Gong, Hui; Luo, Qingming (Frontiers in Neuroinformatics, 2016)
The recognition of brain nuclei is the basis for localizing brain functions. Traditional histological research, represented by atlas illustration, achieves the goal of nucleus boundary recognition by manual delineation on limited numbers of sections, but it has become increasingly difficult to extend this handmade method to delineating brain regions and nuclei on tens of thousands of sections from large datasets acquired by the recently developed single-cell-resolution imaging techniques for the whole brain. Here, we propose a method based on a closed cubic spline (CCS), which can automatically segment the boundaries of nuclei that differ to a relatively high degree in cell density from the surrounding areas on the basis of a manually segmented initial boundary and has been validated on model images and Nissl-stained microimages of mouse brain. It has also been validated on the segmentation of target outlines on MRI or CT images. The proposed method for the automatic extraction of nucleus boundaries would greatly accelerate the illustration of high-resolution brain atlases.