This invention belongs to the field of
bioinformatics, specifically relating to a
deep learning-based method for perspective 3D reconstruction of serous epidermis. The method includes the following steps: First, stained continuous sections of the serous epidermis are acquired; second, the stained continuous sections are input into a reference point analysis module to obtain reference point descriptions; then, the reference point descriptions and stained continuous sections are processed by a continuous section registration module to obtain aligned continuous sections; subsequently, the aligned continuous sections are spatially filled in using a continuous section interpolation module to obtain filled continuous sections; finally, the filled continuous sections are spatially modeled using a continuous section stitching module to obtain an initial 3D reconstruction, and then a perspective 3D reconstruction is obtained using a section background removal module and a 3D data denoising module. This invention proposes a complete set of 3D reconstruction methods for serous epidermis based on
deep learning technology, capable of performing perspective 3D reconstruction under a conventional
microscope, possessing both color layer information and internal structure representation capabilities.