Spotiflow: accurate and efficient spot detection for fluorescence microscopy with deep stereographic flow regression
Identification of spot-like structures in large, noisy microscopy images is a crucial step for many life-science applications. Imaging-based spatial transcriptomics (iST), in particular, relies on the precise detection of millions of transcripts in low signal-to-noise images. Despite recent advances...
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| Hauptverfasser: | , , , , , , , , , , , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
06 June 2025
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| In: |
Nature methods
Year: 2025, Jahrgang: 22, Heft: 7, Pages: 1495-1504 |
| ISSN: | 1548-7105 |
| DOI: | 10.1038/s41592-025-02662-x |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1038/s41592-025-02662-x Verlag, lizenzpflichtig, Volltext: https://www.nature.com/articles/s41592-025-02662-x |
| Verfasserangaben: | Albert Dominguez Mantes, Antonio Herrera, Irina Khven, Anjalie Schlaeppi, Eftychia Kyriacou, Georgios Tsissios, Evangelia Skoufa, Luca Santangeli, Elena Buglakova, Emine Berna Durmus, Suliana Manley, Anna Kreshuk, Detlev Arendt, Can Aztekin, Joachim Lingner, Gioele La Manno & Martin Weigert |
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| 245 | 1 | 0 | |a Spotiflow |b accurate and efficient spot detection for fluorescence microscopy with deep stereographic flow regression |c Albert Dominguez Mantes, Antonio Herrera, Irina Khven, Anjalie Schlaeppi, Eftychia Kyriacou, Georgios Tsissios, Evangelia Skoufa, Luca Santangeli, Elena Buglakova, Emine Berna Durmus, Suliana Manley, Anna Kreshuk, Detlev Arendt, Can Aztekin, Joachim Lingner, Gioele La Manno & Martin Weigert |
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| 520 | |a Identification of spot-like structures in large, noisy microscopy images is a crucial step for many life-science applications. Imaging-based spatial transcriptomics (iST), in particular, relies on the precise detection of millions of transcripts in low signal-to-noise images. Despite recent advances in computer vision, most of the currently used spot detection techniques are still based on classical signal processing and require tedious manual tuning per dataset. Here we introduce Spotiflow, a deep learning method for subpixel-accurate spot detection that formulates spot detection as a multiscale heatmap and stereographic flow regression problem. Spotiflow supports 2D and 3D images, generalizes across different imaging conditions and is more time and memory efficient than existing methods. We show the efficacy of Spotiflow by extensive quantitative experiments on diverse datasets and demonstrate that its increased accuracy leads to meaningful improvements in biological insights obtained from iST and live imaging experiments. Spotiflow is available as an easy-to-use Python library as well as a napari plugin at https://github.com/weigertlab/spotiflow. | ||
| 650 | 4 | |a Fluorescence in situ hybridization | |
| 650 | 4 | |a Image processing | |
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| 700 | 1 | |a Khven, Irina |e VerfasserIn |4 aut | |
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| 700 | 1 | |a Tsissios, Georgios |e VerfasserIn |4 aut | |
| 700 | 1 | |a Skoufa, Evangelia |e VerfasserIn |4 aut | |
| 700 | 1 | |a Santangeli, Luca |e VerfasserIn |4 aut | |
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| 700 | 1 | |a Manley, Suliana |e VerfasserIn |4 aut | |
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| 700 | 1 | |a La Manno, Gioele |e VerfasserIn |4 aut | |
| 700 | 1 | |a Weigert, Martin |e VerfasserIn |4 aut | |
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