🤖 AI Summary
To address the low segmentation accuracy of small-sized, irregular, and anatomically complex abdominal organs, this paper proposes a novel deep learning framework integrating multi-axis frequency-domain representation with a dual-attention mechanism. Methodologically, we design a Multi-Axis External Weighting Block (MEWB) to jointly model global structural and local boundary information in the frequency domain, and enhance the Dual-Attention module (DA+) to strengthen cross-domain feature fusion between spatial and frequency domains, thereby mitigating the encoder-decoder semantic gap. Built upon the TransUNet architecture, our method incorporates frequency-domain multi-axis representation learning, depthwise separable convolutions, and joint spatial-channel attention. Evaluated on the Synapse dataset, it achieves a mean Dice Similarity Coefficient (DSC) of 81.32% and Hausdorff Distance (HD) of 16.35 mm—improving DSC by 3.84% and reducing HD by 15.34 mm over the baseline—and outperforms state-of-the-art methods.
📝 Abstract
Accurate abdominal multi-organ segmentation is critical for clinical applications. Although numerous deep learning-based automatic segmentation methods have been developed, they still struggle to segment small, irregular, or anatomically complex organs. Moreover, most current methods focus on spatial-domain analysis, often overlooking the synergistic potential of frequency-domain representations. To address these limitations, we propose a novel framework named FMD-TransUNet for precise abdominal multi-organ segmentation. It innovatively integrates the Multi-axis External Weight Block (MEWB) and the improved dual attention module (DA+) into the TransUNet framework. The MEWB extracts multi-axis frequency-domain features to capture both global anatomical structures and local boundary details, providing complementary information to spatial-domain representations. The DA+ block utilizes depthwise separable convolutions and incorporates spatial and channel attention mechanisms to enhance feature fusion, reduce redundant information, and narrow the semantic gap between the encoder and decoder. Experimental validation on the Synapse dataset shows that FMD-TransUNet outperforms other recent state-of-the-art methods, achieving an average DSC of 81.32% and a HD of 16.35 mm across eight abdominal organs. Compared to the baseline model, the average DSC increased by 3.84%, and the average HD decreased by 15.34 mm. These results demonstrate the effectiveness of FMD-TransUNet in improving the accuracy of abdominal multi-organ segmentation.