SACU-Net: Shape-Aware U-Net for Biomedical Image Segmentation With Attention Mechanism and Context Extraction

With the rapid development of convolutional neural networks in image processing, deep learning has been widely applied to medical image segmentation tasks, including liver, retinal vessels, nuclei, and COVID-19 lesion segmentation. However, the accuracy and interpretability of these methods still need further improvement. This paper proposes a novel Shape-aware U-Net architecture with Attention Mechanism and Context Extraction (SACU-Net) to address the aforementioned issues regarding segmentation performance and shape identification. Our model introduces three major modifications to the standard U-Net: 1) a new context extraction module block (MRC) to capture high-level context features, 2) redesigned skip pathways with spatial and channel attention mechanisms to reduce the semantic gap between the feature maps of encoder and decoder sub-networks, and 3) a novel loss function to enhance segmentation accuracy. We have evaluated SACU-Net against various U-Net variants on four different medical image segmentation tasks: liver segmentation in abdominal CT scans, retinal vessel segmentation, nuclei segmentation, and COVID-19 lesion segmentation. The results demonstrate relative improvements in performance of 7.30%, 5.5%, and 8.40% compared to state-of-the-art U-Net variants.