Neuron reconstruction from fluorescence microscopy images using sequential Monte Carlo estimation

Abstract

Microscopic analysis of neuronal cell morphology is required in many studies in neurobiology. The development of computational methods for this purpose is an ongoing challenge and includes solving some of the fundamental computer vision problems such as detecting and grouping sometimes very noisy line-like image structures. Advancements in the field are impeded by the complexity and immense diversity of neuronal cell shapes across species and brain regions, as well as by the high variability in image quality across labs and experimental setups. Here we present a novel method for fully automatic neuron reconstruction based on sequential Monte Carlo estimation. It uses newly designed models for predicting and updating branch node estimates as well as novel initialization and final tree construction strategies. The proposed method was evaluated on 3D fluorescence microscopy images containing single neurons and neuronal networks for which manual annotations were available as gold-standard references. The results indicate that our method performs favorably compared to state-of-the-art alternative methods.