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

Abstract

This 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. The detection rate, image feature performance, and image quality satisfaction of the two groups of children were compared. The 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). The 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.