Research on image segmentation of acute pancreatitis based on attention mechanism
10.3760/cma.j.cn121382-20240116-00206
- VernacularTitle:基于注意力机制的急性胰腺炎影像分割研究
- Author:
Hong DENG
1
;
Jiali XIAO
;
Wen FENG
;
Yuanzhong ZHU
;
Bo XIAO
;
Wenjing HE
Author Information
1. 川北医学院医学影像学院，南充 637000
- Keywords:
Acute pancreatitis;
Attention mechanism;
Medical image processing;
Pancreas segmentation
- From:
International Journal of Biomedical Engineering
2024;47(2):141-148
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To assess the efficacy of different fusion strategies involving the convolutional block attention module (CBAM) and Unet for automatic pancreas segmentation in enhanced CT images of patients with acute pancreatitis.Methods:A retrospective analysis was conducted on 1 158 patients with acute pancreatitis admitted to the Affiliated Hospital of North Sichuan Medical College between January 1st, 2016 and July 30th, 2021. Among them, 141 patients with first-episode acute pancreatitis were randomly categorized into mild, moderate, and severe cases. The test set comprised 5 mild and 15 severe cases, while the remaining 126 cases were used for training. Within the training set, 20% of the data was randomly allocated as the validation set. Different fusion paths of the CBAM and Unet networks were trained, utilizing the Dice similarity coefficient, Hausdorff distance (HD), and pixel accuracy (PA) as evaluation metrics. The model demonstrating the best performance on the validation set was selected and evaluated on the test set. Additionally, the Unet model was combined with the attention gate attention mechanism (AttentionUnet) in the skip connection, and the ResBlock replaced the original convolution module (ResUnet) in the Unet network. Moreover, the skip connection branch module of feature extraction was integrated with CBAM (ResUnet_CBAM) for comparison.Results:Unet_CBAM achieved better results on the test set with a Dice value of 80.06%, a HD value of 3.765 9 and a PA value of 0.992 3, all surpassing other fusion strategies. The segmentation accuracy of the pancreatic region in CT images of acute pancreatitis patients was notably enhanced compared to Unet and its related variant networks.Conclusions:The Unet network integrated into CBAM after skip connection can better perform pancreatic segmentation on enhanced CT images of patients with acute pancreatitis and can effectively improve the efficiency of relevant personnel in pancreatic segmentation on enhanced CT images of patients with acute pancreatitis.
