Fundus image focus area labeling method based on deep learning

Abstract

The invention discloses a fundus image focus area labeling method based on deep learning, and the method comprises the steps: selecting a sample, and carrying out the preprocessing: carrying out the cutting of an image, and carrying out the horizontal and vertical overturning and normalization processing; constructing a convolutional neural network and a deconvolutional neural network as an imagegenerator, inputting the preprocessed color fundus image, and outputting a corresponding focus probability graph; constructing a convolutional neural network as a discriminator, inputting and generating a focus image and a real focus image, and outputting a probability that the focus image is judged to be a real image; alternately training the generation network and the discrimination network until a satisfaction result can be generated; and marking a focus area in the fundus image according to the generated focus probability graph. The fundus image focus probability graph is generated by using the deep convolutional neural network, and the fundus image focus area is automatically labeled. The automatic labeling can provide an auxiliary basis for the diagnosis of doctors, and meanwhile, the cost of manual labeling can be greatly reduced.

Description

technical field [0001] The invention relates to a deep learning-based labeling method for fundus image lesion regions, which belongs to the field of medical image processing. Background technique [0002] In recent years, with the continuous improvement of medical imaging acquisition equipment and the continuous development of image processing, pattern recognition, machine learning and other disciplines, the field of multidisciplinary medical image processing and analysis has achieved fruitful results. These achievements are of great significance for assisting doctors in making rapid and accurate diagnoses. [0003] Diabetic retinopathy (Diabetic Retinopathy, DR) is a common complication of diabetes and one of the main causes of blindness in middle-aged and elderly people. thing. The rate of blindness can be effectively reduced through regular screening and early diagnosis of diseases. Due to the huge number of people who need to be screened, it is a time-consuming and la...

AI Technical Summary

Problems solved by technology

However, medical annotation images are usually small in scale and expensive to annotate, so we hope to use a simple and direct way to directly generate the annotations of the lesion area from the fundus retinal image to reduce the cost of manual annotation

Images