Objective To retrospectively analyze the emergency complications of the patients after oocyte retrieval with assisted reproductive technology (ART), and analyze the corresponding strategies. Methods The clinical data of patients after oocyte retrieval with ART between January and December 2016 were retrospectively anayzed. The postoperative emergency complications were observed. Results A total of 5 013 patients were included in the study. The common emergency complications after oocyte retrieval included vaginal bleeding in 137 cases (2.73%) , ovarian hyperstimulation syndrome (OHSS) in 35 (0.69%), hematuria caused by bladder injury in 11 cases (0.21%), pelvic infection in 3 (0.06%), and vagal reflex in 2 (0.04%). OHSS was related to age, the number of basal follicles, the number of oviposaccharides and the estradiol level on the day of human chorionic gonadotropin injection, but not related to the body mass index and the number of days of gonadotropin use; which might be misdiagnosed most likely. Conclusions OHSS is one of the common and severe emergency complications after oocyte retrieval with ART, which should be concerned. Active treatment of complications is helpful to reduce the incidence of emergency complications after oocyte retrieval with ART.
Medical image registration plays an important role in medical diagnosis and treatment planning. However, the current registration methods based on deep learning still face some challenges, such as insufficient ability to extract global information, large number of network model parameters, slow reasoning speed and so on. Therefore, this paper proposed a new model LCU-Net, which used parallel lightweight convolution to improve the ability of global information extraction. The problem of large number of network parameters and slow inference speed was solved by multi-scale fusion. The experimental results showed that the Dice coefficient of LCU-Net reached 0.823, the Hausdorff distance was 1.258, and the number of network parameters was reduced by about one quarter compared with that before multi-scale fusion. The proposed algorithm shows remarkable advantages in medical image registration tasks, and it not only surpasses the existing comparison algorithms in performance, but also has excellent generalization performance and wide application prospects.