3D neuronal image segmentation of the mouse brain

Neurons are highly morphologically complex, the whole brain image is huge, and strong noise, discontinuous signals, and mutual interference of signals often appear in neural images. The above problems have greatly increased the difficulty of neuron morphological calculation and analysis, so neuron morphology computation and analysis is widely regarded as one of the most challenging computational tasks in computational neuroscience. This paper introduces 3D-segmentation-net, an end-to-end learning method that can automatically segment 3D neuron images from sparse annotations. In automated segmentation validation experiments, we achieved an average IoU of 0.86. The network was trained from scratch and has not been optimized for this application. It is suitable for any mouse brain image segmentation task, and realizes automatic segmentation, tracking, fusion and real-time manual revision of a series of tracking schemes for massive neural images.