Sclera lens OCT (Optical Coherence Tomography) image tear layer segmentation model, method and equipment based on deep learning

The invention discloses a sclera lens OCT image tear layer segmentation model, method and equipment based on deep learning. The method is mainly used for simultaneously extracting structures of tear layers and corneas with sclera lenses in anterior segment OCT images of eyes, and can also be used for segmenting medical images with low tissue contrast, serious imaging artifacts and the like and assisting quantization of morphological characteristics of spatial positioning of image interested targets. According to the method, a deep learning model is used for carrying out image segmentation on a cornea OCT image with a sclera lens so as to extract structural information of three levels of the sclera lens, a tear layer and a cornea in the OCT image. Firstly, a training data set is collected and labeled for training a deep learning model. Then, the trained model is used for segmenting a new cornea OCT image, and through combination of image processing and a deep learning algorithm, automatic cornea structure segm