A lung nodule segmentation model based on the transformer with multiple thresholds and coordinate attention

Accurate lung nodule segmentation is fundamental for the early detection of lung cancer. With the rapid development of deep learning, lung nodule segmentation models based on the encoder-decoder structure have become the mainstream research approach. However, during the encoding process, most models have limitations in extracting edge and semantic information and in capturing long-range dependencies. To address these problems, we propose a new lung nodule segmentation model, abbreviated as MCAT-Net. In this model, we construct a multi-threshold feature separation module to capture edge and texture features from different levels and specified intensities of the input image. Secondly, we introduce the coordinate attention mechanism, which allows the model to better recognize and utilize spatial information when handling long-range dependencies, enabling the deep network to maintain its sensitivity to nodule positions. Thirdly, we use the transformer to fully capture the long-range dependencies, further enhancing the global information integration of the network. The proposed method was verified on the LIDC-IDRI and LNDb datasets. The Dice similarity coefficient (DSC) values achieved were 88.29% and 78.51%, and the sensitivities were 86.33% and 75.05%, respectively. The experimental results demonstrated its high practical value for the early diagnosis of lung cancer.