Research And Application Of Deep Learning Based Gastric Endoscopic Image Restoration

With the rapid development of modern medical images,imaging technology of medical equipment plays an increasingly important role in medical diagnosis.Doctors can observe the internal tissues of the human body through the device to analyze and judge the patient’s disease.However,in the imaging process of the device,due to the influence of hardware equipment,environment and other factors,the quality of the medical image is often uneven.For example,biological mucus inside the human body can make the image contain reflective areas,which can affect the efficiency of the doctor’s film reading and diagnosis of patients.Therefore,the study of endoscopic image repair with reflective areas is of great significance to assist clinical diagnosis.In this thesis,deep learning technology is used to repair the reflective region of gastric endoscopy image.The main contents of the thesis are as follows:(1)Aiming at the problems such as boundary artifacts and distorted structures caused by traditional image repair methods in gastric endoscopy image repair,a twostage network MRI-Net with step-by-step repair was proposed to repair the reflective area of gastric endoscopy image.First,the original image was detected for reflection,and the mask marking the reflective area was obtained.On the basis of the traditional reflection detection method,fixed threshold segmentation was added and the union was made to optimize the reflection detection results.Then,the mask and the original image channel are splicing together,and the initial stage restoration image is obtained by fast Fourier convolution processing.The image is input into the second stage restoration network with the global branch and the local branch with the semantic attention layer for feature extraction.The features extracted from the two branches are merged and upsampled to obtain the final restoration result.Finally,a spectral normalized Markov discriminator is used to train a free-form image repair network.The experimental results show that MRI-Net has good effect on endoscope image reflection repair.(2)The optimization of the network model based on gated attention convolution is studied.A gated attention convolution module(GAC)was proposed to solve the problem of discontinuity at the generated image boundary caused by MRI-Net repair of endoscopy images and to improve the quality of the repaired images.The structure of the first-stage repair network was kept unchanged,and the conventional convolution of the second-stage network was replaced by a gated attention convolution module composed of gated convolution and attention mechanism,and the whole network formed a new network architecture OMRI-Net.The edge of the reflective region of the original endoscope image was successfully restored better,and the quality of the restored image was improved.(3)Based on the above research,the endoscope image reflective repair system was designed and implemented.The system can repair the endoscopic images with reflective areas taken by the endoscopic instrument very well,and the use of the system can assist medical workers to analyze the patient’s condition,greatly improving the efficiency of the doctor’s film reading.