CSP-UNet: A Lightweight Network for Hand X-Ray Image Segmentation

Hand X-ray image segmentation is an important step in automated radiographic image analysis. However, conventional U-shaped segmentation networks often have relatively high model complexity, while variations in grayscale distributions across hand X-ray images may affect segmentation performance. To address these issues, this study proposes a lightweight hand X-ray image segmentation network, CSP-UNet (Cross-Stage Partial U-Net). The network integrates cross-stage partial feature processing into the U-Net encoder-decoder framework to reduce redundant feature computation and the number of model parameters while preserving effective feature representation. In addition, an adaptive Gaussian histogram-matching strategy is employed to reduce variations in grayscale distributions across X-ray images. CSP-UNet was evaluated on a dataset comprising 2000 hand X-ray images and compared with Classic U-Net, Res-UNet, Attention U-Net, and Swin U-Net. Experimental results show that CSP-UNet maintained comparable segmentation performance, achieving a Dice coefficient of 0.9927, PA of 0.9839, MPA of 0.9810, and mIoU of 0.9542, while requiring only 20.01 M parameters. Compared with Classic U-Net, CSP-UNet maintained a comparable Dice coefficient (0.9927 vs. 0.9910) while reducing the parameter count from 69.1 M to 20.01 M, corresponding to a reduction of approximately 71.04%. These results indicate that CSP-UNet maintains comparable segmentation performance while substantially reducing model complexity, offering a favorable trade-off between segmentation performance and model size for hand X-ray image segmentation.