[Analysis of pancreatic segmentation algorithm based on deep learning to improve pancreatic critical region segmentation ability on dual-phase CT].

Abstract

Objective: To investigate the segmentation effects of the deep learning method on CT in the arterial phase and venous phase respectively by using subjective and objective evaluation system, and to investigate the factors that affect the difference between arterial phase and venous phase pancreas segmentation and the related factors affecting the venous pancreas segmentation. Method: A total of 218 cases of pancreatic CT scan data in the Department of Radiology of Peking Union Medical College Hospital from January to November 2019 were retrospectively collected. Each case contained images of arterial and venous phases, and the data were randomly divided into training set (139 cases), validation set (20 cases) and test set (59 cases) according to the ratio of the training and verification set to the test set of 7∶3. The two-stage global local progressive fusion network was trained on the training set, the model parameters of the optimal segmentation effect were found on the validation set, and the test set was predicted and the results were evaluated subjectively and objectively. The Likert 5-point scale was used for subjective evaluation based on the critical regions between pancreas and peripheral organs, while the Dice similarity coefficient (DSC) was used for objective evaluation. The paired t test or Wilcoxon paired rank test was used to compare the differences of subjective and objective scores of the arterial phase and venous phase. Results: For the critical regions of the pancreas at the duodenum, duodenal jejunal flexure, left adrenal gland, portal vein, superior mesenteric vein, splenic artery and splenic vein, the median number of subjective scores in arterial phase were 4(4, 5), 5(4, 5), 5(4, 5), 4(4, 5), 5(4, 5), 5(5, 5) and 4(3, 5)points respectively, the median number(first quartile, third quartile) of subjective scores in venous phase were 4(4, 4), 5(4, 5), 5(4, 5), 5(4, 5), 5(5, 5), 4(3, 4) and 5(5, 5) points respectively,there were statistically significant differences of the median number(first quartile, third quartile) of the subjective scores between the arterial and venous phase for the critical regions of the pancreas at the organs described above (all P<0.05). DSC in the venous phase was slightly higher than that in the arterial phase and the difference was not statistically significant (DSC: 0.932 vs 0.921, P=0.952). Subjective scores in venous phase of the pancreas and duodenal jejunum, stomach, and left adrenal gland with fat gaps were 4.64,4.68 and 4.63 points respectively, and those of the group without fat gaps were 4.56,4.62 and 4.56 points respectively, there were statistically significant differences of the subjective scores in venous phase of the groups with fat gaps or not between the pancreas and the organs described above (t=2.147, 2.112, 2.277, all P<0.05). Except the spleen, the density differences between the critical regions of the pancreas and other surrounding organs were statistically significant in arterial phase and venous phase segmentation (all P<0.05). Conclusion: Dual-phase CT was used to construct a deep learning automatic pancreas segmentation model, and the segmentation effect was evaluated subjectively and objectively. Subjective evaluation was helpful to improve the ability to segment the critical regions of the pancreas in the future.