SACU-Net: Shape-Aware U-Net for Biomedical Image Segmentation With Attention Mechanism and Context Extraction
Yinuo Cao, Yong Cheng · IEEE Access · 2025
With the advent of convolutional neural network in image processing, deep learning has been used for medical image segmentation, such as liver, retina vessel, Nuclei and COVID-19 lesion segmentation, etc. Even though, the accuracy and the interpretablility of these methods still need to be futher improved. In this paper, we propose a novel Shape-aware U-Net architecture with Attention Mechanism and Context Extraction named SACU-Net to address the aforementioned issues regarding segment performance and shape identification. Our model mainly includes three modifications on the standard U-Net: 1) design a new context extraction module block (MRC) to capture high-level context feature, 2) re-design the skip pathways with spatial and channel attentions which reduce the semantic gap between the feature maps of encoder and decoder sub-networks, 3) a novel loss function was proposed to emphasize the segmentation accuracy on segmentation tasks. We have evaluated the SACU-Net in comparison with U-Net Variants on four different medical image segmentation tasks: liver segmentation in abdominal CT scans, retina vessel, Nuclei and COVID-19 lesion, which obtains a relative improvement in performance of 7.30%, 5.5% and 8.40% compared with the state-of-the-art U-Net Variant.