Automatic　segmentation　of　pulmonary　nodules　on　computed　tomography　(CT)　images　can　aid　in　the　early　diagnosis　and　treatment　of　lung　cancer.　However,　this　task　is　challenging　due　to　the　heterogeneity　among　different　types　of　pulmonary　nodules　and　the　similarity　in　grayscale　values　between　the　nodules　and　surrounding　lung　tissue.　To　effectively　improve　the　pulmonary　nodule　segmentation,　we　propose　a　new　boundary-aware　dual-branch　neural　network.　In　the　first　phase,　our　edge　guidance　module　integrates　multi-level　encoder　outputs　to　capture　key　boundary　information　and　suppress　non-target　edges.　The　second　phase　uses　a　feature　fusion　module　to　dynamically　combine　high-level　semantic　information　and　boundary　features.　Finally,　a　dynamic　convolutional　attention　module　refines　skip　connections　through　dynamic　spatial　attention,　identifying　key　spatial　regions　and　generating　attention　maps　to　highlight　the　most　distinctive　and　representative　features.　Extensive　experiments　on　the　publicly　available　LIDC-IDRI　lung　dataset　demonstrate　that　our　method　significantly　improves　the　segmentation　performance　of　pulmonary　nodules　in　CT　images　compared　to　existing　segmentation　techniques.　©　The　Author(s),　under　exclusive　license　to　Springer　Nature　Singapore　Pte　Ltd.　2025.