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Quantitative evaluation of software packages for single-molecule localization microscopy


The quality of super-resolution images obtained by single-molecule localization microscopy (SMLM) depends largely on the software used to detect and accurately localize point sources. In this work, we focus on the computational aspects of super-resolution microscopy and present a comprehensive evaluation of localization software packages. Our philosophy is to evaluate each package as a whole, thus maintaining the integrity of the software. We prepared synthetic data that represent three-dimensional structures modeled after biological components, taking excitation parameters, noise sources, point-spread functions and pixelation into account. We then asked developers to run their software on our data; most responded favorably, allowing us to present a broad picture of the methods available. We evaluated their results using quantitative and user-interpretable criteria: detection rate, accuracy, quality of image reconstruction, resolution, software usability and computational resources. These metrics reflect the various tradeoffs of SMLM software packages and help users to choose the software that fits their needs.

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Figure 1: Construction of the bio-inspired data.
Figure 2: Accuracy versus detection rate for each tested software.
Figure 3: Rendering of software results versus ground truth at various scales.
Figure 4: Illustration of an assessment report.


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We thank N. Olivier for providing the experimental data and R. Nieuwenhuizen for his technical assistance in running the Fourier ring correlation. We thank also P. Thévenaz for critical reading and for his assistance in writing the manuscript. We thank the participants in the ISBI 2013 localization microscopy challenge: S. Anthony, S. Andersson, T. Ashley, D. Baddeley, K. Bennett, J. Boulanger, N. Brede, L. Dai, L. Fiaschi, F. Gruell, G. Hagen, R. Henriques, A. Herbert, S. Holden, E. Hoogendoorn, B. Huang, Z.-L. Huang, A. Kechkar, K. Kim, M. Kirchgessner, U. Koethe, P. Krizek, M. Lakadamyali, Y. Li, K. Lidke, R. McGorty, L. Muresan, R. Parthasarathy, B. Rieger, H. Rouault, M. Sauer, J.-B. Sibarita, I. Smal, A. Small, S. Stahlheber, Y. Tang, Y. Wang, S. Watanabe, S. Wolter, J.C. Ye and C. Zimmer. This work was supported by the Biomedical Imaging Group, the School of Engineering at the Ecole Polytechnique Fédérale de Lausanne, the European Research Council (ERC) FUN-SP project (267439), the ERC Starting Grant PALMassembly (243016) and the Eurobioimaging Project (WP11).

Author information

Authors and Affiliations



D.S., H.K., T.P., J.M. and N.S. conceived the project. D.S. developed the project and organized the challenge with contribution from all authors. D.S. and H.K. wrote the code for the simulated data and analyzed the results. S.M. and M.U. directed the project. D.S. and H.K. wrote the manuscript with input from all authors.

Corresponding author

Correspondence to Daniel Sage.

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Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Comparative results for detection rate and accurary for the long sequence datasets.

Plot the detection rate (Jaccard) vs. the accuracy of every software and plot the efficiency line for every dataset. The crosses indicates the results of the lower-bound software (CenterOfGravity) that we have developped.

Supplementary Figure 2 Comparative results for detection rate and accurary for the high-density datasets.

Plot the detection rate (Jaccard) vs. the accuracy of every software and plot the efficiency line for every dataset.

Supplementary Figure 3 Correlation of the quantitative assessement criteria.

Plots of the 2 by 2 cross-correlation of the four quantitative criteria, detection rate (JAC), accuracy (ACC), image quality assessment (SNR), and image resolution (FRC). The results of all evaluated software are plotted in a different color for every dataset, the 3 long-sequence datasets (LS) and the high-density datasets (HD). The position of the crosses indicates the average per dataset and the length of its arms is equal to the standard deviation.

Supplementary information

Combined PDF

Supplementary Figures 1–3 and Supplementary Notes 1–3 (PDF 1570 kb)

Supplementary Data 1

Visual results, comparison to ground-truth (ZIP 60834 kb)

Supplementary Data 2

Numerical results, values, grades, and ranking (XLSX 177 kb)

Supplementary Software 1

Java software to compare two sets of localization (ZIP 78 kb)

Supplementary Video 1

HD1 100 frames of the contest dataset HD1 (MOV 599 kb)

Supplementary Video 2

HD2 100 frames of the contest dataset HD2 (MOV 818 kb)

Supplementary Video 3

HD3 100 frames of the contest dataset HD3 (MOV 1218 kb)

Supplementary Video 4

LS1 100 frames of the contest dataset LS1 (MOV 744 kb)

Supplementary Video 5

LS2 100 frames of the contest dataset LS2 (MOV 965 kb)

Supplementary Video 6

LS3 100 frames of the contest dataset LS3 (MOV 1895 kb)

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Sage, D., Kirshner, H., Pengo, T. et al. Quantitative evaluation of software packages for single-molecule localization microscopy. Nat Methods 12, 717–724 (2015).

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