SimpleSTORM: a fast, self-calibrating reconstruction algorithm for localization microscopy
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Although there are many reconstruction algorithms for localization microscopy, their use is hampered by the difficulty to adjust a possibly large number of parameters correctly. We propose SimpleSTORM, an algorithm that determines appropriate parameter settings directly from the data in an initial self-calibration phase. The algorithm is based on a carefully designed yet simple model of the image acquisition process which allows us to standardize each image such that the background has zero mean and unit variance. This standardization makes it possible to detect spots by a true statistical test (instead of hand-tuned thresholds) and to de-noise the images with an efficient matched filter. By reducing the strength of the matched filter, SimpleSTORM also performs reasonably on data with high-spot density, trading off localization accuracy for improved detection performance. Extensive validation experiments on the ISBI Localization Challenge Dataset, as well as real image reconstructions, demonstrate the good performance of our algorithm.
KeywordsLocalization microscopy STORM reconstruction Matched filter Noise normalization Self-calibration
This research was supported by contract research “Methoden für die Lebenswissenschaften” of the Baden-Württemberg Stiftung. We are grateful to Mike Heilemann, Varun Venkataramani and Benjamin Flottmann for providing the raw data of the images presented in this paper, as well as for giving many helpful comments on the algorithm. We also thank the organizers of the ISBI Localization Microscopy Challenge for the permission to use their artificial data.
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