DMFU-Net: a dual-domain attention network with multi-scale feature for pancreas and pancreatic tumor CT segmentation

Abstract

Abstract. The pancreas and its associated tumors typically exhibit minuscule spatial proportions, high heterogeneity, and poorly contrasted with surrounding tissues, rendering automated segmentation from computed tomography scans highly challenging. To address these problems, we propose DMFU-Net, a spatial–frequency attention-guided segmentation framework built upon a U-Net-style architecture. Integrating multi-scale standard and atrous convolutions, the encoder balances global context and local details to extract rich semantic information. For further feature refinement, the bottleneck layer leverages multi-scale receptive fields to aggregate cross-scale features, significantly fortifying deep semantic representations. In the final stage, the dual-domain attention mechanism within the decoder leverages spatial and frequency domain complementarity, sustaining semantic consistency while precisely delineating minuscule structures. Experimental results on the Medical Segmentation Decathlon Task07_Pancreas dataset show that DMFU-Net achieves Dice similarity coefficient (DSC) scores of 56.79% and 82.53% for tumors and the pancreas, respectively. Notably, for ultra-small-volume tumors (<0.015%), our method achieves a 14.4% DSC gain over nnU-Net. These results demonstrate its superior reliability in challenging small-lesion cases and its potential value for downstream clinical quantitative analysis.