SPA-DCN-NET: A Gated Multi-Scale Local Contrast Normalization Network for Ultrasound Image Segmentation of Liver

Segmentation of the liver in ultrasound images is a critical task in medical image analysis, yet it remains challenging due to acoustic speckle noise, brightness instability, and deformations caused by probe pressure. To address these problems, this study presents SPA-DCN-NET, a lightweight framework that integrates three synergized components: first, a learnable gated local contrast normalization (Gated LCN) module utilizes a sigmoid soft-gate mechanism to dynamically fuse LCN-enhanced features with original features, effectively stabilizing the features for training. Second, a spatial pyramid attention (SPA) module applies multi-scale context aggregation, transforming the clear features provided by Gated LCN into deformable convolutional networks. Third, these features guide deformable convolutional networks to adaptively adjust sampling grids, ensuring precise delineation of irregular liver boundaries in ultrasound images. Experimental results demonstrate that our SPA-DCN-NET achieved mean IoU scores of 83.52%, 75.57%, 73.85%, and 85.94% across the four datasets, respectively. These results are all higher than those obtained by UNet, nnUNet, and ResUNet. The metrics indicate that our SPA-DCN-NET is more adaptable to the ultrasonic medical environment compared to other existing medical segmentation networks, and it is a recommended network of image analysis for ultrasound abdominal scans.

@article{Jian2025,
  title     = {SPA-DCN-NET: A Gated Multi-Scale Local Contrast Normalization Network for Ultrasound Image Segmentation of Liver},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {11},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Su Ming Jian and Afizan Bin Azman and Afizan.Azman@taylors.edu.my},
  doi       = {10.14569/IJACSA.2025.0161154},
  url       = {https://doi.org/10.14569/IJACSA.2025.0161154}
}