The invention relates to the technical field of
retina analysis, and discloses a fundus
optical coherence tomography image feature point automatic positioning method, which comprises the following steps: acquiring
optical coherence tomography images of a fundus
disease patient at different
disease course stages, constructing an
image database containing a
training set and a
verification set, and determining the feature points of the fundus
optical coherence tomography image on the basis of the
image database. And carrying out feature layer segmentation on the image by adopting a U-Net
convolutional neural network. And determining a
blood vessel shadow position in the fundus optical coherence
tomography image and acquiring a boundary of the
blood vessel shadow position based on the
pigment epithelium gray scale curve. And determining the position of a seed point and the size of a subarea by combining an important layer boundary and a vessel shadow boundary, and assisting
digital image correlation to realize
retina full-field deformation measurement. According to the method, through deep fusion of
deep learning and an
image processing technology, the problem of automatic selection of the seed points in the fundus OCT image is solved, the efficiency, precision and reliability of
retinal deformation measurement are remarkably improved, and a powerful technical tool is provided for mechanism research and
clinical diagnosis and treatment of fundus diseases.