Progressive Learning for Neuronal Population Reconstruction from Optical Microscopy Images

Reconstruction of 3D neuronal populations from optical microscopy images is essential to investigate neural pathways and functions. This task is challenging because of the low signal-to-noise ratio and non-continuous intensities of neurite segments in optical microscopy images. Recently, significant improvement has been made on neuron reconstruction due to the development of deep neural networks (DNNs). Training such a DNN usually relies on a large number of images with voxel-wise annotations, and annotating these 3D images is very costly in terms of both finance and labor. In this paper, we propose a progressive learning strategy to take advantages of both traditional neuron tracing methods and deep learning techniques. Traditional neuron tracing techniques, which do not require expensive manual annotations for dense neurites, are employed to produce pseudo labels for neuron voxels. With the pseudo labels, a deep segmentation network is trained to learn discriminative and comprehensive features for neuron voxel extraction from noisy backgrounds. The neuron tracing module and the segmentation network can be mutually complemented and progressively improved to reconstruct more complete neuronal populations without using manual annotations. Moreover, we build a dataset called "VISoR-40" that consists of 40 optical microscopy 3D images from mouse cortical regions to demonstrate the superior performance of our progressive learning method.