Application of CT images based on the optimal atlas segmentation algorithm in the clinical diagnosis of Mycoplasma Pneumoniae Pneumonia in Children

Objective: Use of optimal Atlas segmentation algorithm to study the imaging signs of mycoplasma pneumonia with multi-slice spiral CT (HRCT), and to explore the value of HRCT in the diagnosis and efficacy in evaluation of mycoplasma pneumonia in children. Methods: The study retrospectively analyzed 72 patients diagnosed with mycoplasma pneumonia in our hospital from January 2017 to January 2019. The imaging data and clinical data of 72 patients were collected. The optimal Atlas segmentation algorithm was used to analyze the characteristics of CT examination, and the value of CT in the diagnosis of mycoplasma pneumonia and the evaluation of curative effect was summarized. Results: Among all patients, 37 cases were unilateral lesions, 35 cases were bilateral lesions, 19 cases were in the left upper lobe, 24 cases were in the left lower lobe, 21 cases were in the right upper lobe, 13 cases were in the right middle lobe, 25 The lesion was located in the right lower lobe. The main CT findings of the lesions before treatment were large patchy, spot-shaped shadows, and strip-shaped or ground-glass shadows. After treatment, the main CT findings of the lesions were reduced lesion density and reduced lesion range. Conclusion: CT can clearly show the pulmonary lesions of mycoplasma pneumonia, and its unique imaging signs can improve the clinical diagnosis accuracy. In addition, CT scans can evaluate the treatment effect according to the changes in the characteristics of the lesion, which has important value for the evaluation of the effect for clinical diagnosis and efficacy evaluation of mycoplasma pneumonia. doi: https://doi.org/10.12669/pjms.37.6-WIT.4860 How to cite this:Fu X, Yang N, Ji J. Application of CT images based on the optimal atlas segmentation algorithm in the clinical diagnosis of Mycoplasma Pneumoniae Pneumonia in Children. Pak J Med Sci. 2021;37(6):1647-1651. doi: https://doi.org/10.12669/pjms.37.6-WIT.4860 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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

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.