The development of computer-aided diagnosis (CAD) systems for antinuclear autoantibodies tests in indirect immunofluorescence using HEp-2 cells has attracted growing research efforts in the last years. Although in this field many CAD solutions extract information from objects detected within the images, cell segmentation is an issue far from being solved. This work introduces a segmentation pipeline based on an active contour method, as none in the literature on HEp-2 cell segmentation does. Our proposal can detect objects whose boundaries are not necessarily defined by gradient, and this choice plays a relevant role for HEp-2 images with different fluorescence intensities and different staining patterns. The performances of the approach is tested not only on a public benchmark dataset with 18 images, but also on other 24 images that we made publicly available. Furthermore, its performances are compared with those provided by other state-of-the-art segmentation methods.