Super-resolution segmentation network for reconstruction of packed neurites

Abstract Neuron reconstruction can provide the quantitative data required for measuring the neuronal morphology and is crucial in the field of brain research. However, the difficulty in reconstructing packed neuritis, wherein massive labor is required for accurate reconstruction in most cases, has not been resolved. In this work, we provide a fundamental pathway for solving this challenge by proposing the use of the super-resolution segmentation network (SRSNet) that builds the mapping of the neurites in the original neuronal images and their segmentation in a higher-resolution space. SRSNet focuses on enlarging the distances between the boundaries of the packed neurites producing the high-resolution segmentation images. Thus, in the construction of the training datasets, only the traced skeletons of neurites are required, which vastly increase the usability of SRSNet. From the results of the experiments conducted in this work, it has been observed that SRSNet achieves accurate reconstruction of packed neurites where the other state-of-the-art methods fail.