ArticleThe British journal of radiology2022
Combination of UNet++ and ResNeSt to classify chronic inflammation of the choledochal cystic wall in patients with pancreaticobiliary maljunction.
Article in The British journal of radiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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2 citing papers in PubMed, 4 citations in OpenAlex.
- Deep learning radiomics nomogram for preoperatively identifying moderate-to-severe chronic cholangitis in children with pancreaticobiliary maljunction: a multicenter study.BMC medical imaging · 2025Article
- Development of a simplified model and nomogram in preoperative diagnosis of pediatric chronic cholangitis with pancreaticobiliary maljunction using clinical variables and MRI radiomics.Insights into imaging · 2023Article
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5 authors at 4 institutions in 1 country.
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No grant is acknowledged in the PubMed record.
Abstract
objectivesThe aim of this study was to establish an automatic classification model for chronic inflammation of the choledoch wall using deep learning with CT images in patients with pancreaticobiliary maljunction (PBM).
methodsCT images were obtained from 76 PBM patients, including 61 cases assigned to the training set and 15 cases assigned to the testing set. The region of interest (ROI) containing the choledochal lesion was extracted and segmented using the UNet++ network. The degree of severity of inflammation in the choledochal wall was initially classified using the ResNeSt network. The final classification result was determined per decision rules. Grad-CAM was used to explain the association between the classification basis of the network and clinical diagnosis.
resultsSegmentation of the lesion on the common bile duct wall was roughly obtained with the UNet++ segmentation model and the average value of Dice coefficient of the segmentation model in the testing set was 0.839 ± 0.150, which was verified through fivefold cross-validation. Inflammation was initially classified with ResNeSt18, which resulted in accuracy = 0.756, sensitivity = 0.611, specificity = 0.852, precision = 0.733, and area under curve (AUC) = 0.711. The final classification sensitivity was 0.8. Grad-CAM revealed similar distribution of inflammation of the choledochal wall and verified the inflammation classification.
conclusionsBy combining the UNet++ network and the ResNeSt network, we achieved automatic classification of chronic inflammation of the choledoch in PBM patients and verified the robustness through cross-validation performed five times. This study provided an important basis for classification of inflammation severity of the choledoch in PBM patients. ADVANCES IN KNOWLEDGE: We combined the UNet++ network and the ResNeSt network to achieve automatic classification of chronic inflammation of the choledoch in PBM. These results provided an important basis for classification of choledochal inflammation in PBM and for surgical therapy.
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