Purpose Spinal X-ray image segmentation faces several challenges, such as complex anatomical structures, large variations in scale, and blurry or low-contrast boundaries between vertebrae and surrounding tissues. These factors make it difficult for traditional models to achieve accurate and robust segmentation. To address these issues, this study proposes MDWC-Net, a novel deep learning framework designed to improve the accuracy and efficiency of spinal structure identification in clinical settings. Methods MDWC-Net adopts an encoder–decoder architecture and introduces three modules—MSCAW, DFCB, and BIEB—to address key challenges in spinal X-ray image segmentation. The network is trained and evaluated on the Spine Dataset, which contains 280 X-ray images provided by Henan Provincial People’s Hospital and is randomly divided into training, validation, and test sets with a 7:1:2 ratio. In addition, to evaluate the model’s generalizability, further validation was conducted on the Chest X-ray dataset for lung field segmentation and the ISIC2016 dataset for melanoma boundary delineation. Results MDWC-Net outperformed other mainstream models overall. On the Spine Dataset, it achieved a Dice score of 89.86% ± 0.356, MIoU of 90.53% ± 0.315, GPA of 96.82% ± 0.289, and Sensitivity of 96.77% ± 0.212. A series of ablation experiments further confirmed the effectiveness of the MSCAW, DFCB, and BIEB modules. Conclusion MDWC-Net delivers accurate and efficient segmentation of spinal structures, showing strong potential for integration into clinical workflows. Its high performance and generalizability suggest broad applicability to other medical image segmentation tasks.
Gu et al. (Fri,) studied this question.