Abstract

Neuronal soma segmentation is essential for morphology quantification analysis. Rapid advances in light microscope imaging techniques have generated such massive amounts of data that time-consuming manual methods cannot meet requirements for high throughput. However, touching soma segmentation is still a challenge for automatic segmentation methods. In this paper, we propose a soma segmentation method that combines the Rayburst sampling algorithm and ellipsoid fitting. The improved Rayburst sampling algorithm is used to detect the soma surface; the ellipsoid fitting method then refines jagged sampled soma surface to generate smooth ellipsoidal shapes for efficient analysis. In experiments, we validated the proposed method by applying it to datasets from the fluorescence micro-optical sectioning tomography (fMOST) system. The results indicate that the proposed method is comparable to the manual segmented gold standard with accurate soma segmentation at a relatively high speed. The proposed method can be extended to large-scale image stacks in the future.