論文ID: 2025EAL2036
Precise and automatic segmentation of pulmonary lesions is crucial for assisting pulmonologists in accurate diagnosis and decision-making. Despite advances in deep learning, segmenting pulmonary nodules remains challenging due to factors like small lesions, irregular boundaries, and data imbalance. We propose an edge detail enhancement method (EDC-UNet) for pulmonary nodule segmentation, which integrates deformable convolutional layers to improve flexibility for various lesion morphologies and dilated convolution-based residual blocks to enhance feature extraction. Additionally, a Sobel-based detail supervision module in the decoder helps capture low-level spatial details, improving segmentation of blurred edges. Extensive experiments on the LIDC-IDRI and LUNA16 datasets demonstrate that EDC-UNet outperforms other models, highlighting its potential for medical image analysis.
