LAUSR

Objective It aimed to explore the application of the microscopic hyperspectral technique in motor and sensory nerve classification. Methods The self-developed microscopic hyperspectral acquisition system was applied to collect the data of anterior and posterior spinal cord sections of white rabbits. The joint correction algorithm was employed to preprocess the collected data, such as noise reduction. On the basis of pure linear light source index, a new pixel purification algorithm based on cross contrast was proposed to extract more regions of interest, which was used for feature extraction of motor and sensory nerves. Besides, the ML algorithm was employed to classify motor and sensory nerves based on feature extraction results. Results The joint correction algorithm was adopted to preprocess the data collected by the microscopic hyperspectral technique, so as to eliminate the influence of the incident light source and the system and improve the classification accuracy. The axon and myelin spectrum curves of the two kinds of nerves in the stained specimens had the same trend, but the values of all kinds of spectrum of sensory nerves were higher than those of motor nerves. However, the myelin sheath spectrum curves of motor nerves in the unstained specimens were greatly different from the curves of sensory nerves. The axon spectrum curves had the same trend, but the axon spectrum values of sensory nerves were higher than those of motor nerves. The ML algorithm had high accuracy and fast speed in motor and sensory nerve classification, and the classification effect of stained specimens was better than that of unstained specimens. Conclusion The microscopic hyperspectral technique had high feasibility in sensory and motor nerve classification and was worthy of further research and promotion.