HMSAM-UNet: A Hierarchical Multi-Scale Attention Module-Based Convolutional Neural Network for Improved CT Image Segmentation

Abstract

While modern deep learning methods have made significant progress in medical image segmentation, some challenges remain, including accurately capturing features at multiple scales, limited ability to detect critical regions, and susceptibility to noise and background interference. To address these challenges, a new neural network called HMSAM-UNet is introduced in this work. A novel module, the Hierarchical Multi-Scale Attention Module (HMSAM), was designed in HMSAM-UNet to improve the precision and accuracy of CT image segmentation significantly. Specifically, HMSAM integrates the Hierarchical Attention Mechanism and Inception Module via residual connections. The Hierarchical Attention Mechanism can highlight important regions by learning attention weights, dramatically enhancing the model’s ability to perceive critical areas for more accurate localization and segmentation of target structures in CT images. Meanwhile, incorporating the Inception module effectively strengthens the network’s capacity to capture multi-scale features, substantially improving the model’s ability to comprehend the structural characteristics of CT images. The results show that the average loss achieved by the proposed model has a 50.04% reduction compared to the original U-Net architecture. Furthermore, compared to other deep learning models such as FCN, DeepLabV3, PSPNet, Unet, UNet++, and SegNet, the model proposed in this work attains an average Dice coefficient of 98.72, and an average IoU score of 97.46 on the three datasets, both of which are the highest among all compared models.

Keywords

• CT image segmentation
• hierarchical attention mechanism
• inception module
• multi-scale
• critical region perception