Three-dimensional segmentation of airways from multi-slice computed tomography (MSCT) is a key point in the development of computer-aided tools for respiratory investigation. The expected benefits are related to diagnosis improvement of airway pathologies, preoperative planning and follow-up. The segmentation issue becomes even more challenging with regard to the high variability of the MSCT image acquisition in clinical practice due to the different CT scanners used and the various protocols (mainly at low dose). This paper develops a generic and automated 3D airway segmentation approach able to deal with a large spectrum of MSCT protocols by exploiting a combined morphologicalaggregative methodology. The proposed method was independently assessed by an external group of medical experts in the context of a segmentation challenge, on a database consisting of 20 thorax MSCT datasets. This database included acquisitions from several clinical centers equipped with different CT scanners and using various protocols. The evaluation results show a good performance of the developed approach in terms of airway segments detection accuracy, in the context of highly variable MSCT input data.