Research On Missing Image Data Imputation Based On Longitudinal Lung Cancer Image

The traditional diagnosis of lung cancer is mainly to find the location of pulmonary nodules and judge their benign and malignant by means of manual film reading,which has a high requirement for the experience of physicians.At the same time,the heavy work is likely to cause diagnostic fatigue,and even lead to missed diagnosis or misdiagnosis,and delay the best treatment period of patients.The emergence of computer-aided diagnosis technology has realized the automatic diagnosis of pulmonary nodules,reducing the work of doctors,and alleviating the problem of different medical levels in different regions to a certain extent.However,at present,most of the computer-aided diagnosis technologies related to lung cancer are aimed at the images of lung lesions in independent period,usually the detection and automatic diagnosis of lung nodules are carried out on the lung images.However,the formation of pulmonary nodules is a long-term process,and only the study of images in a single period cannot fully understand the characteristics of the lesion area.If we can study the whole process from the appearance of lung lesions to the gradual development over time,we will have a deeper understanding of the evolution of lung cancer.However,due to the difficulty in obtaining the image sequence of the complete course of the disease,it is a great challenge to carry out research through longitudinal lung cancer image sequence.This study first analyzed the current methods of pulmonary nodules detection,and made an understanding of studies using longitudinal pulmonary lesion image data,proposed an improved method of pulmonary nodules detection,and used incomplete long-term image data to study the evolution of pulmonary nodules features.(1)In view of the small size of early pulmonary nodules,which is difficult to detect,and the fact that there are many interference information similar to the shape and size of pulmonary nodules in the original lung CT images,it is easy to cause the problem of wrong detection results.Based on the existing target detection network framework,this paper makes a further improvement by combining Fathers R-CNN with the feature pyramid network for the detection of lung nodules,especially the screening of lung nodules in the early stage.In addition,a new loss function is proposed,which is helpful for the screening of lung nodules through further constraint and feature extraction.The feasibility of the proposed method is verified by experiments on NLST dataset.(2)Aiming at the problem that it is difficult to obtain the longitudinal pulmonary image sequence at present,this paper proposes a method to estimate the missing period data by using the existing data,which transforms the task of image data estimation into the task of image domain conversion.Through the algorithm of missing pulmonary nodules image synthesis based on generative adversarial network,a single generator and discriminant network can be used to successfully estimate missing data using the existing data sets.A variety of loss functions are used to restrict the effect of the synthetic image,so that the generated pseudo image is more realistic.Finally,the proposed method is proved to have higher visual quality through the control experiment.