利用深度学习实现腹盆部CT图像范围及期相分类:临床验证研究

目的:探讨基于深度学习的分类模型对腹盆部CT图像范围及期相进行自动分类的可行性.方法:回顾性搜集本院2019年10月14日-2019年10月18日PACS中连续416例患者的腹盆部CT图像(数据集A).按照扫描范围分为腹部、腹盆部、盆部三类,按照扫描期相分为平扫、动脉期、门静脉期、延迟期和排泄期五类.以3D-ResNe...

Deep learning for classification of range and phase of abdominal and pelvic CT scanning:a prospectivevalidation study in clinical workflow

Objective:To explore the feasibility of a classification model established by deep learning for classifying the scan range and dynamic contrast enhanced phase of abdominal and pelvic CT images.Methods:The abdominal and pelvic CT images of 416 consecutive patients were retrospectively collected from Oct 14,2019 to Oct 18,2019(dataset A).According to the scanning range,all subjects were divided into three groups:abdomen,abdomen and pelvis,pelvis.And according to the scanning phase,they were divided into five groups:plain scan,arterial phase,portal venous phase,delayed phase and exctretory phase.The CT image range and phase classification model were trained based on 3D-ResNet.The model was used to predict 657 consecutive CT images of abdomen and pelvis from January 1,2020 to January 3,2020(Dataset B).The classification results of radiologist were taken as the gold standard,the confusion matrix was used to evaluate the classification efficiency of the model.Results:In Dataset B,the accuracy of the scanning range classification model in the abdomen,abdomen and pelvis,pelvis was 95.7%(243/254),98.4%(362/368)and 94.3%(33/35),respectively.In the abdomen images of Dataset B,the accuracy of plain scan,arterial and portal venous phase was 100.0%(77/77),97.6%(82/84)and 100.0%(11/11),respectively.In the abdomen and pelvis images of Dataset B,the accuracy of plain scan,arterial phase,portal venous phase,delayed phase and exctretory phase was 96.6%(144/149),100.0%(9/9),100.0%(106/106),66.7%(44/66)and 100.0%(32/32),respectively.In the pelvis images of Dataset B,the accuracy of pelvic scan phase classification model in plain scan,portal venous phase,delayed phase and exctretory phase was 100.0%(13/13),70.0%(7/10),88.9%(8/9)and 100.0%(1/1),respectively.Conclusion:The accuracy of abdominal and pelvic CT image classification model established by deep learning is accurate and can meet the cli-nical requirements.