Automated quantitative assessment of pediatric blunt hepatic trauma by deep learning-based CT volumetry

European Journal of Medical Research

Research

Wanliang Guo¹  Dashuang Li²  Shungen Huang²  Zhiyong Zhou³  Yakang Dai³  Xusheng Qian⁴ 

[1] Department of Radiology, Children’s Hospital of Soochow University, 215025, Suzhou, China;Pediatric Surgery, Children’s Hospital of Soochow University, 215025, Suzhou, China;Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, 215163, Suzhou, China;Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, 215163, Suzhou, China;School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, 230026, Hefei, China;

关键词: Pediatric blunt hepatic trauma;    Deep learning;    Quantitative assessment;    Contrast-enhanced CT;   

DOI  :  10.1186/s40001-022-00943-1

 received in 2022-09-10, accepted in 2022-12-08,  发布年份 2022

来源: Springer

【 摘 要 】

BackgroundTo develop an end-to-end deep learning method for automated quantitative assessment of pediatric blunt hepatic trauma based on contrast-enhanced computed tomography (CT).MethodsThis retrospective study included 170 children with blunt hepatic trauma between May 1, 2015, and August 30, 2021, who had undergone contrast-enhanced CT. Both liver parenchyma and liver trauma regions were manually segmented from CT images. Two deep convolutional neural networks (CNNs) were trained on 118 cases between May 1, 2015, and December 31, 2019, for liver segmentation and liver trauma segmentation. Liver volume and trauma volume were automatically calculated based on the segmentation results, and the liver parenchymal disruption index (LPDI) was computed as the ratio of liver trauma volume to liver volume. The segmentation performance was tested on 52 cases between January 1, 2020, and August 30, 2021. Correlation analysis among the LPDI, trauma volume, and the American Association for the Surgery of Trauma (AAST) liver injury grade was performed using the Spearman rank correlation. The performance of severity assessment of pediatric blunt hepatic trauma based on the LPDI and trauma volume was evaluated using receiver operating characteristic (ROC) analysis.ResultsThe Dice, precision, and recall of the developed deep learning framework were 94.75, 94.11, and 95.46% in segmenting the liver and 72.91, 72.40, and 76.80% in segmenting the trauma regions. The LPDI and trauma volume were significantly correlated with AAST grade (rho = 0.823 and rho = 0.831, respectively; p < 0.001 for both). The area under the ROC curve (AUC) values for the LPDI and trauma volume to distinguish between high-grade and low-grade pediatric blunt hepatic trauma were 0.942 (95% CI, 0.882–1.000) and 0.952 (95% CI, 0.895–1.000), respectively.ConclusionsThe developed end-to-end deep learning method is able to automatically and accurately segment the liver and trauma regions from contrast-enhanced CT images. The automated LDPI and liver trauma volume can act as objective and quantitative indexes to supplement the current AAST grading of pediatric blunt hepatic trauma.

【 授权许可】

CC BY   
© The Author(s) 2022

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Fig. 4