Research On Medical Image Segmentation Algorithm Based On Multi Scale Domain Adaptation

In recent years,with the rapid development of productivity and the gradual improvement of living standards,health issues have become a key issue of people’s attention.In particular,retinal fundus vascular disease has become a major ophthalmological disease that plagues millions of people in the world today.The unique medical properties of the length,width,curvature,branching pattern and angle of retinal blood vessels can be used for diagnosis,screening,treatment and evaluation of various cardiovascular and ophthalmological diseases.With the rapid development of deep learning,deep neural networks have demonstrated powerful feature extraction capabilities in computer vision tasks and applications,especially in the field of medical image segmentation,with significant effects.However,in many medical application environments,the collected medical images are often of high resolution,and there are more noises in the images.At the same time,due to the influence of light and equipment,some inconspicuous or incomplete objects in the image come from the dominant The context of the salient object will not help to mark the unobvious object.Secondly,the target object in the medical image has complex features.Due to privacy and the huge cost of expert annotation,it is difficult to collect enough labeled data,resulting in depth The learning algorithm cannot obtain enough labeled data to train a reliable model.This paper proposes an unsupervised domain adaptive adversarial learning method based on multi-scale fusion.The local features extracted by the feature extractor and the global output obtained by the decoder are simultaneously input into the domain discriminator,and the medical image is considered from multiple scales.Feature distribution reduces the distribution difference between the source domain and the target domain,thereby improving the performance of image segmentation.Firstly,it analyzes the characteristics of medical images,and combines the problems of fewer samples,too much noise,and difficulty in labeling medical images of fundus retinal blood vessels,and proposes a method that combines deep neural networks with adversarial learning.At the same time,it also considers the effect of multi-scale fusion on the model.The impact is finally applied to unsupervised domain adaptation to achieve satisfactory semantic segmentation on medical images.