A deep convolutional network model based on 3D V-Net can automatically recognize and segment pancreas and its tumors
CHEN Fei
LI Maolin
JIANG Yuting
LI Kang'an
Abstract：Objective To explore the effectiveness and feasibility of the deep convolutional neural network model,based on V-Net,for automatic recognition and segmentation of the pancreas and its tumors.Methods A retrospective analysis was conducted on the enhanced CT imaging data of 186 patients with pathologically confirmed pancreatic cancer who visited First People's Hospital Affiliated to Shanghai Jiaotong University Medical College from May 2012 to November 2019.After screening,a total of 108 cases of pancreatic cancer were included,and 37 cases of normal pancreas during the same period were randomly collected for comparison,resulting in a final dataset of 145 cases for this study.This paper employed a five-fold cross-validation method and manually annotated regions of interest on arterial phase CT images,including the pancreatic head and neck,body and tail,and tumors.The model's ability to identify pancreatic tumors was evaluated by calculating metrics such as sensitivity,specificity,F1 score,and Kappa consistency verification was performed.Dice coefficient was used to quantitatively assess the model's segmentation capability,and visual results were obtained for further evaluation.Results The V-Net based model for identifying pancreatic tumors has a sensitivity of 0.852,a specificity of 1.000,a positive predictive value of 1.000,a negative predictive value of 0.698,and an F1 score as high as 0.920.The consistency verification shows that the Kappa coefficient is 0.746(P<0.05).In the segmentation task,the mean Dice for pancreatic tumors,pancreatic body and tail,pancreatic head and neck were 0.722±0.290,0.602±0.175,0.567±0.200,respectively.Conclusion We constructed a deep convolutional network model based on V-Net,which successfully achieved automatic identification and segmentation of the pancreas and tumors.Our findings demonstrated the effectiveness and feasibility of this approach,offering robust support for the exploration of artificial intelligence applications in the field of pancreatic tumor research.
Keywords：pancreatic tumorV-Netdeep learningconvolutional neural networkartificial intelligenceautomatic segmentation
Publication Date：2024-11-28
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：6( 1170-1175 )