Objective:To propose a processing method of medical image segmentation based on U-Net network with residual connection under single source domain environment,so as to improve the limited diversity of data samples of medical image in the single-source domain environment.Methods:The medical image data under the single-source domain environment were amplified through image transformation,contrast adjustment,and noise addition.The U-Net network was used as the basic structure,and multiple residual connection modules were introduced to achieve reuse of feature.And then,the features at shallow layer were directly transmitted to deep layer,and features at different levels were utilized for segmentation.The input features were added to the features that were processed by partial network layers,thus better learned the features in the data,and constructed medical image segmentation model based on the U-Net network with residual connection.The segmentation model was trained by using the gradient descent method,and the combination of that and the Generalized Dice Loss(GDL)function.Results:The processing method of medical image segmentation based on the U-Net network can effectively achieve segmentation of medical image.Even under low signal-to-noise ratio conditions and facing to different types of segmentation tasks of medical image,the DSC value was greater than 0.90.Conclusion:The processing method of medical image segmentation based on the U-Net network in this study can effectively enhance recognition ability for the features of complex lesions,and increase the segmentation precise of medical images,and improve the accuracy of clinical diagnosis and the effectiveness of treatment.

Objective:To propose a processing method of medical image segmentation based on U-Net network with residual connection under single source domain environment,so as to improve the limited diversity of data samples of medical image in the single-source domain environment.Methods:The medical image data under the single-source domain environment were amplified through image transformation,contrast adjustment,and noise addition.The U-Net network was used as the basic structure,and multiple residual connection modules were introduced to achieve reuse of feature.And then,the features at shallow layer were directly transmitted to deep layer,and features at different levels were utilized for segmentation.The input features were added to the features that were processed by partial network layers,thus better learned the features in the data,and constructed medical image segmentation model based on the U-Net network with residual connection.The segmentation model was trained by using the gradient descent method,and the combination of that and the Generalized Dice Loss(GDL)function.Results:The processing method of medical image segmentation based on the U-Net network can effectively achieve segmentation of medical image.Even under low signal-to-noise ratio conditions and facing to different types of segmentation tasks of medical image,the DSC value was greater than 0.90.Conclusion:The processing method of medical image segmentation based on the U-Net network in this study can effectively enhance recognition ability for the features of complex lesions,and increase the segmentation precise of medical images,and improve the accuracy of clinical diagnosis and the effectiveness of treatment.

Objective:To assess the values of computed tomography(CT)equipment in whole life cycle and operation status from multi-dimension based on real world data,and to quantify the performance,operation status,and benefit analysis of medical equipment,so as to assist to grasp the operation status of CT equipment for hospital.Methods:Depended on the viewpoint of real-world data,economic benefits,metrology and performance data of 5 pieces of CT equipment,which included type A,type B,type C,type D and type E,used in hospitals in the last 4 years were collected.Type A,type B and type C were all imported CT equipment,and type A and type B were the same model.Both type D and type E were domestic CT equipment.The analytic hierarchy process(AHP)was used to assign the weights of each data,and form effectively multi-dimensional evaluation system.Results:The value of consistency ratio(CR)of the hierarchical weight allocation matrix was 0.042 that was less than 0.1,which met the requirement of consistency test.The scores of the comprehensive values the 5 pieces of CT equipment of type A,type B,type C,type D and type E were respectively 68.10,30.35,39.50,60.99 and 39.76.There was a significant difference between the two pieces of imported CT equipment of type A and type B of the same model in different use environments.The score of type D of domestic CT equipment was higher than those of the type B and type C of imported CT equipment,respectively.Conclusion:Under suitable use environments,domestic CT equipment can replace imported equipment.The AHP method can be used in the assessment of CT equipment value.

Radioactive Protective aprons are used to reduce radiation dose during fluoroscopy and exposure.Tears,cracks and holes can be detected by X-ray inspection.By establishing the rejection criteria,routine inspection and renew,the radioactive safety can be acheieved.