Research On Medical Image Segmentation Algorithm Based On Deep Learning

In recent years,thanks to the rapid development of science and technology,a variety of medical image imaging technologies have emerged and are widely used in major medical and health systems.The analysis of a large number of medical images generated subsequently requires a lot of resources.With the rise of deep learning technology,more and more people begin to turn their attention to medical image analysis methods based on deep learning,and medical image segmentation is one of the key issues.At present,methods based on deep learning have achieved good results in many natural image segmentation tasks.However,unlike natural images,medical images still have some problems in segmentation tasks due to their own characteristics.Including but not limited to lack of high-quality labeled data,high labor costs,small data volume,small segmentation targets,and partial labeling.This paper mainly focuses on the problem of partial labeling.The main contents of this paper are as follows:1.The current status of medical image segmentation methods based on deep learning is briefly introduced.First,the characteristics of medical images and the storage format of medical images are introduced,and then the traditional medical image segmentation methods and deep learning-based medical image segmentation methods are introduced,and the current problems and challenges faced by medical image segmentation are pointed out.direction of research.2.Aiming at the partial labeling problem in medical image segmentation,this paper proposes a multi-task medical image segmentation method Dy HNet based on dynamic convolution.This method introduces dynamic filter convolution,which can handle multi-task medical image segmentation more efficiently and flexibly.question.The network model of this method consists of three modules,an encoder-decoder module with a U-shaped network structure to extract features and generate pre-segmentation maps,a task controller module to guide the network to handle specific tasks,and a dynamic filter volume The product module is used to generate the convolution kernel parameters and complete the segmentation task.The method performs well on multiple datasets,verifying its effectiveness.3.This paper proposes a multi-task medical image segmentation method Dy HPNet based on Prototype,which introduces the concept of Prototype.Prototype is a high representation of the distribution characteristics of a certain category of data,which can well guide the network model to perform multi-task segmentation.This paper first introduces the calculation method of Prototype,then adds Prototype to Dy HNet,and improves each module in it,making the entire network model more robust.Finally,comparative experiments and ablation experiments are carried out on multiple datasets to verify the effectiveness of the algorithm.