Reliable 3D detection of diffraction-limited spots in fluorescence microscopy images is an important task in subcellular observation. Generally, fluorescence microscopy images are heavily degraded by noise and non-specifically stained background, making reliable detection a challenging task. In this work, we have studied the performance and parameter sensitivity of eight recent methods for 3D spot detection. The study is based on both 3D synthetic image data and 3D real confocal microscopy images. The synthetic images were generated using a simulator modeling the complete imaging setup, including the optical path as well as the image acquisition process. We studied the detection performance and parameter sensitivity under different noise levels and under the influence of uneven background signal. To evaluate the parameter sensitivity, we propose a novel measure based on the gradient magnitude of the F 1 score. We measured the success rate of the individual methods for different types of the image data and found that the type of image degradation is an important factor. Using the F 1 score and the newly proposed sensitivity measure, we found that the parameter sensitivity is not necessarily proportional to the success rate of a method. This also provided an explanation why the best performing method for synthetic data was outperformed by other methods when applied to the real microscopy images. On the basis of the results obtained, we conclude with the recommendation of the HDome method for data with relatively low variations in quality, or the Sorokin method for image sets in which the quality varies more. We also provide alternative recommendations for high-quality images, and for situations in which detailed parameter tuning might be deemed expensive. V C 2015 International Society for Advancement of Cytometry Key terms fluorescence microscopy; 3D imaging; diffraction-limited spot detection; parameter sensitivity RELIABLE spot detection in 3D confocal microscopy is an important task in cell observation. The objects of interest such as proteins or other biomolecules (e.g., sequences of nucleic acids) are typically fluorescently tagged and they appear as bright, diffraction-limited particles in 3D images. For example, the proteins of interest can be genetically modified to become fluorescent while keeping their function (1), or fluorescent antibodies can be attached to proteins (2). The sequences of nucleic acids can be labeled by Fluorescence In-Situ Hybridization (FISH), which allows us to visualize parts of chromosomes such as telomeres or centromeres or even individual genes (3). It is known that the spatial distribution of subcellular objects is related to their function, and thus reliable 3D spot detection is of paramount importance in many biological applications, for example, in particle tracking (4), studies on the spatial arrangement of genes (5-7), in telomere studies (8-11), kinetochore studies (12), and in co-localization studies (13). Reliable spot detection is also essential in the Photoac...