Splenic injury region segmentation and severity prediction based on deep learning of abdomi-nal plain CT
Zhou Liping
Luo De
Li Bo
Cheng Zhitao
Leng Yanlin
Tang Yong
Liu Yi
Bai Changsong
Gu Guangqiang
Su Song
Abstract：Objective To construct a deep learning model based on abdominal CT plain scans and to ex-plore its value in automatically segmenting the region of splenic injury in closed abdominal injuries and initially as-sessing the severity of splenic injury.Methods A total of 4,290 abdominal plain CT images were retrospectively collected from 122 patients with splenic injuries diagnosed as closed abdominal injuries and 240 patients with normal intact spleens,of whom 192 were male patients and 170 were female,with a mean age of 53.2(5-90)years.Three deep learning models,UNet,FCN-ResNet50,and FCN-ResNet101,were used in the study of automatic segmentation of splenic injury regions.The two-step method(segmenting the spleen contour before recognizing the damaged re-gion)and the one-step method(directly recognizing the damaged region)were used for the construction of the deep learning model,respectively,and the accuracy(ACC),intersection over union(IoU),and the DICE coefficient(Dice)were used as evaluation metrics for comparison.In the study to assess the severity of spleen injury,four deep learning models,DenseNet,CNN,ResNet101,and ResNet18,were used.Splenic injuries were dichotomized into mild(grades Ⅰ and Ⅱ)and moderately severe(grades Ⅲ-Ⅴ)groups on complete CT cross-sectional images and auto-matically segmented splenic injury regions,respectively,and the classification performance of the models was evalua-ted by the area under the curve(AUC)of the receiver operating characteristic curves(ROC curves)and their 95%CI,ACC,sensitivities,and specificities.Results The model FCN-ResNet101,which uses the one-step method ap-proach to complete splenic injury region segmentation,had the highest ACC for splenic injury region segmentation(IoU:0.713,ACC:0.887,Dice:0.801).The model ResNet18,which used direct input for complete image recogni-tion,had the best overall performance for spleen injury severity prediction(AUC:0.834,95%CI:0.607-1.000,ACC:0.770,Sensitivity:0.885,Specificity:0.561).Conclusion FCN-ResNet101,a deep learning model based on abdominal plain CT,has better automatic segmentation performance for spleen injury.The deep learning model Res-Net18 has better classification performance when assessing the severity of spleen injury.Both deep learning models have good prospects for clinical applications.
Keywords：Spleen traumaBlunt abdominal traumaCT plain scans
Publication Date：2025-03-15
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：9( 177-185 )