Segmentation model of pancreas from abdominal CT based on feedforward attention ConvNeXt
10.13929/j.issn.1003-3289.2025.03.025
- VernacularTitle:基于前馈注意力ConvNeXt模型分割腹部CT中的胰腺
- Author:
Wenhan ZHANG
1
;
Yongxiong WANG
;
Fubin ZENG
;
Yangsen CAO
Author Information
1. 上海理工大学光电信息与计算机工程学院,上海 200093
- Publication Type:Journal Article
- Keywords:
pancreas;
tomography,X-ray computed;
image segmentation
- From:
Chinese Journal of Medical Imaging Technology
2025;41(3):466-472
- CountryChina
- Language:Chinese
-
Abstract:
Objective To observe the performance of ConvNeXt architecture model(SC2-Net)integrated with feedforward attention(FA)for segmentation of pancreas from abdominal CT images.Methods 3D abdominal CT images of 80 healthy adults(Dataset 1)and 68 patients with pancreatic lesions(Dataset 2)were included.ConvNeXt network model was established and enhanced by introducing a FA mechanism,a scalable convolution block(SCB)and a feature gating(FG)module into the encoder section.The performance of the model for segmenting pancreas were comparatively evaluated with other models(Swin UNETR,nnFormer,UNETR,TransBTS models based on Transformer and 3D UX-NET model based on ConvNeXt),while conduct ablation experiments were performed on the added modules.Results SC2-Net accurately segmented pancreas from abdominal CT images,with Dice similarity coefficient(DSC),95％Hausdorff distance(HD95)and the mean surface distance(MSD)of 0.92±0.01,(1.08±0.05)mm and(2.12±0.01)mm in Dataset 1,respectively.The DSC and HD95 of SC2-Net segmentation of pancreas were both superior to those of other models.In Dataset 2,SC 2-Net achieved DSC,HD95 and MSD of 0.82±0.03,(3.35±0.36)mm and(0.87±0.15)mm,respectively,surpassing all other models.SC2-Net achieved complete pancreas segmentation in both datasets,whereas other models demonstrated under-segmentation or mis-segmentation.FA module significantly improved segmentation performance when integrated into the baseline network.Conclusion SC2-Net could improve segmentation of pancreas from abdominal CT images.
