A neuron image segmentation method based Deep Boltzmann Machine and CV model. (April 2021) - He, Fuyun; Huang, Xiaoming; Wang, Xun; Qiu, Senhui; Jiang, F.; Ling, Sai Ho
Highlights: A neuron image segmentation method by combining Chan-Vest (CV) model with Deep Boltzmann Machine (DBM) is proposed. A generative model is used to model and generate the target shape. The shape priori information is fused to assist neuron image segmentation. Two 3D-EM datasets are adopted with the best performance in Variation of Information (VoI) and Adaptive Rand Index (ARI). Experimental results show the fusion algorithm characterizes the sub-microstructure information with high segmentation accuracy and robustness. Abstract: Neuron image segmentation has wide applications and important potential values for neuroscience research. Due to the complexity of the submicroscopic structure of neurons cells and the defects of the image quality such as anisotropy, boundary loss and blurriness in electron microscopy-based (EM) imaging, and one faces a challenge in efficient automated segmenting large-scale neuron image 3D datasets, which is an essential prerequisite front-end process for the reconstruction of neuron circuits. Here, a neuron image segmentation method by combining Chan-Vest (CV) model with Deep Boltzmann Machine (DBM) is proposed, and a generative model is used to model and generate the target shape, it take this as a prior information to add global target shape feature constraint to the energy function of CV model, and the shape priori information is fused to assist neuron image segmentation. We applied our method to two 3D-EM datasets from different types of nerve tissue and achieved the best performance consistently across two classical evaluation metrics of neuron segmentation accuracy, namely Variation of Information (VoI) and Adaptive Rand Index (ARI). Experimental results show that the fusion algorithm has high segmentation accuracy, strong robustness, and can characterize the sub-microstructure information of neuron images well.