The assessment of pulmonary hyperpolarised(HP)-gas MR images is instrumental in identifying potential pathologies, directing treatment, or monitoring disease progression. HP-gas images quantify the amount of gas concentration, whose distribution within the lungs is analysed. A key role in this process is played the main airways that are identified for quality control and excluded for the analysis. Currently, this task is performed manually and existing deep-learning (DL) applications do not provide an explicit labelling of the airways. A specific tailoring of a well-known DL approach was developed with the aim of replacing the manual editing thanks to its good performance.