Research And Implementation Of Recognition System For Characteristics Of Pronuclear-Stage Embryo Based On Perceptual Occlusion Pronucleus

With the development of medical diagnosis technology and the improvement of the theory of embryonic development,in vitro fertilization and embryo transfer technology has become the preferred treatment of infertility and other related diseases after four generations.Among them,morphological diagnosis is the key to successful transplantation before embryonic implant,and the features of prokaryotic embryos are strictly defined as the primary basis for diagnosis.In this thesis,the embryo feature recognition algorithm is implemented by computer vision technology.Compared with the traditional recognition scheme,the algorithm in this paper can automatically and effectively segment the main region of embryo and obtain the accuracy close to the physician in the recognition of the primary features of prokaryotic embryos.Finally,it provides a user-friendly medical diagnosis and treatment auxiliary system.The main research contents of this thesis are as follows:1.Multi-Scale Embryo Subject Segmentation Algorithm Based on Encoder-Decoder Structure.In the original image,the embryonic body only accounts for only 25.56%,and the remainder has a large amount of impurities which affect the recognition of embryonic features.Based on encoder-decoder structure of traditional semantic segmentation algorithm,we combined with atrous convolution,human receptive field and attention mechanism to proposed a multi-scale embryo subject segmentation algorithm that can sufficiently fused different levels of semantic characteristics,the algorithm is composed of a feature extraction network,a multi-scale feature fusion network,and a prediction network.In the end,the performance test of the related algorithm is completed on the datasets of newly produced retinal vessel and the subject of embryo.2.Occlusion Prokaryotic Perception Algorithm Based on Polar Coordinate System.This thesis focuses on the identification of the number of pronucleus in embryos at the prokaryotic stage,and research is based on Mask-R-CNN,a two-stage instance segmentation model.In order to solve the problem of false detection and concealing the pronucleus missed detection,a variety of data preprocessing,data enhancement and strategies for training model are designed.At the same time,the concept of polar coordinate system is introduced and to construct the occlusion prokaryotic perception network.The network is different from the traditional segmentation algorithm of the pixel-by-pixel classification,which transforms instance segmentation into instance center point prediction and intensive distance regression.The strategy can effectively improve the detection rate of occlusion pronucleus.Finally,the algorithm completes the comparative experiments and performance tests on the prokaryotic segmentation data set.3.Design and Implementation of Embryonic Features Recognition System.This thesis designs a reasonable system architecture,database table structure,and core functional module based on the actual needs related to research.The system is implemented by Java,Python dual backend,and Java terminal is mainly responsible for designing graphical interfaces and implementing logical functions such as data operation;Python terminal is mainly responsible for implemented core algorithms such as prokaryotic embryo feature recognition.Users and permission information,system configuration,and historical results are persistently stored in MySQL database.Finally,this thesis implements a system that includes functions such as user management,data access,embryo subject segmentation,and prokaryotic embryo feature recognition.The system has good data security and scalability.