[Lung nodule segmentation method based on multiscale feature interaction and coordinate information]

For pulmonary nodules in computed tomography (CT) images, which exhibit complex morphology and blurred boundaries, existing segmentation methods still fall short in modelling cross-level dependencies of multi-scale features, thereby limiting their performance in pulmonary nodule segmentation tasks. To address these challenges, this paper proposes a semantic segmentation method for pulmonary nodules based on multiscale feature interaction and cross-level coordinate attention (MFI-CLCA). This U-shaped network incorporated three architectures: a convolutional neural network (CNN), a Transformer, and Mamba. During the encoding phase, combining CNN and Mamba learning paradigms capured both global and local information in the input data. The convolutional component extracted complex boundary features of the target by combining multi-scale convolutional operations with adaptive fusion operations. Global and local multi-head attention mechanisms were introduced in the bottleneck layer and decoding phase respectively to model these hierarchical feature dependencies. The skip-connection section incorporated a multi-level coordinate attention module to adaptively focus on the information being passed through. Experimental results on the Lung Image Database Consortium (LIDC) dataset demonstrated that this approach achieved Dice scores of 90.52% and sensitivity of 91.93%, which outperforms existing state-of-the-art methods and validates its effectiveness for lung nodule segmentation tasks.