3D Cnn-Based Soma Segmentation from Brain Images at Single-Neuron Resolution

Neuron segmentation is an important task for automatic analyses of brain images that are of huge volume. Previous methods for neuron segmentation rely on handcrafted image features, and have difficulty in coping with high-resolution, low signal-to-noise-ratio brain images. Convolutional neural network (CNN) has achieved remarkable success in natural image segmentation, but CNN requires accurately labeled data for training that are difficult to achieve on brain images of huge volume. In this paper, we present a weakly supervised learning strategy to deal with the inaccurate training data problem, and thus adopt 3D CNN to perform automatic soma segmentation from brain images. We test our method on our own collected mouse brain images that are of single-neuron resolution, and results show that 3D CNN-based method outperforms the traditional methods by a significant margin.