An efficient 3D extraction and reconstruction method for myelinated axons of mouse cerebral cortex based on mixed intelligence

Abstract

Accurate reconstruction of the 3D morphology and spatial distribution of myelinated axons in mouse brains is very important for understanding the mechanism and dynamic behavior of long-distance information transmission between brain regions. However, it is difficult to segment and reconstruct myelinated axons automatically due to two reasons: the amount of it is huge and the morphology of it is different between brain regions. Traditional artificial labeling methods usually require a large amount of manpower to label each myelinated axon slice by slice, which greatly hinders the development of the mouse brain connectome. In order to solve this problem and improve the reconstruction efficiency, this paper proposes an annotation generation method that takes the myelinated axon as prior knowledge, which can greatly reduce the manual labeling time while reaching the level of manual labeling. This method consists of three steps. Firstly, the 3D axis equation of myelinated axons is established by sparse axon artificial center point labels on slices, and the region to be segmented is pre-extracted according to the 3D axis. Subsequently, the U-Net network was trained by a small number of artificially labeled myelinated axons and was used for precise segmentation of output by the last step, so as to obtain accurate axon 2D morphology. Finally, based on the segmentation results, the high-precision 3D reconstruction of axons was performed by imaris software, and the spatial distribution of myelinated axons in the mouse brain was reconstructed. In this paper, the effectiveness of this method was verified on the dataset of high-resolution X-ray microtomography of the mouse cortex. Experiments show that this method can achieve an average MIoU 81.57, and the efficiency can be improved by more than 1400x compared with the manual labeling method.