Optimal Atlas Segmentation on CT Images for Diagnosis of Pediatric Mycoplasma Pneumonia

Abstract

*is work aimed to explore the clinical application value of CT imaging technology based on the optimal Atlas segmentation algorithm (OASA) in the diagnosis of pediatric mycoplasma pneumonia (MP). Eighty-eight children with MP were selected and divided into group A (CT image based on the OASA) and group B (chest X-ray) according to the diagnosis methods.*e detection rate, image feature performance, and image quality satisfaction of the two groups of children were compared. *e results showed that the detection rate of group A was 97.73% and that of group B was 95.46%, and there was no considerable difference between the two (P> 0.05). *e pleural effusion detection rate of children in group A was evidently superior to that of X-ray group, while the increased bronchovascular shadows’ detection rate was greatly inferior to that of X-ray group (P< 0.05). Comparison results of nodules’ shadows, patchy shadows, acinar parenchyma shadows, and interstitial infiltration between two groups showed that there was no notable difference (P> 0.05). CT image quality satisfaction (98.50%) was higher versus X-ray (79.46%) (P< 0.05). To sum up, CT images based on the OASA can be adopted in the clinical diagnosis of pediatric MP, and CT images were better than chest X-rays.