Accurately detecting cellular structures in fluorescence microscopy is of primary interest for further quantitative analysis such as counting, tracking or classification. We aim at segmenting vesicles in TIRF images. The optimal segmentation scale is automatically selected, relying on a multiscale feature detection stage, and the segmentation consists in thresholding the Laplacian of Gaussian of the intensity image. In contrast to other methods, the threshold is locally adapted, resulting in better detection rates for complex images. Our method is mostly on par with machine learning-based techniques, while offering lower computation time and requiring no prior training. It is very competitive with existing unsupervised detection algorithms.