AMDF-UNet: a boundary-enhanced adaptive multi-scale feature fusion network for abdominal CT multi-organ segmentation

Abstract

Accurate segmentation of abdominal CT images plays a critical role in clinical diagnosis and surgical planning. However, the significant size variations among abdominal anatomical structures — ranging from large organs such as the spleen and spine to small lesions such as urinary stones — together with blurred boundaries between adjacent organs, pose substantial challenges for automated segmentation methods. To address these issues, we propose AMDF-UNet, a novel segmentation network that integrates a Boundary-Enhanced Channel-Prior Convolutional Attention (BE-CPCA) module and an Adaptive Multi-scale Dilated Fusion (AMDF) module into the U-shaped encoder-decoder architecture. The BE-CPCA module is deployed at the bottleneck layer, combining dual-pathway channel attention with a Sobel-operator-based boundary-enhanced spatial attention mechanism to simultaneously capture global channel dependencies and fine-grained boundary features. The AMDF module is embedded in the decoder, employing parallel dilated convolutions with learnable adaptive weights to fuse multi-scale contextual information, thereby enabling effective segmentation of structures with diverse scales. The overall training objective combines Focal Loss, Dice Loss, and a morphology-based Boundary Loss to jointly optimize pixel-level classification, region-level overlap, and boundary-level accuracy. Experiments on a clinical abdominal CT dataset comprising five anatomical categories demonstrate that AMDF-UNet achieves a mean Dice Coefficient of 93.45% and a mean IoU of 92.13%, outperforming mainstream methods including UNet, UNet++, DeepLabV3+, Attention U-Net, ResUNet, EGE-UNet, TransUNet, Swin-UNet, and VM-UNet.