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When combined with a hyperspectral modality, it enables the acquisition of emission spectra that provide insight into the composition of fluorophores. These spectra can be used for fluorophore\u2010based image segmentation; however, accurate spectral separation becomes challenging when fluorophore emission spectra overlap and mix within individual pixels. We present a spatially\u2010corrected density peak clustering (SC\u2010DPC) method that combines phasor transformation with histogram\u2010based density\u2010peak detection and additional spatial correction to achieve accurate and fast clustering. SC\u2010DPC effectively handles large datasets and clusters that are not separable linearly in phasor space while maintaining high clustering accuracy and computational efficiency. Results with synthetic data and experimental multiphoton microscopic data from the mouse retina demonstrate that SC\u2010DPC provides a robust, high\u2010performance approach for hyperspectral fluorescence data segmentation.<\/jats:p>","DOI":"10.1002\/jbio.70351","type":"journal-article","created":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T00:04:27Z","timestamp":1788480267000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Phasor\u2010Based Spatio\u2010Spectral Segmentation for Hyperspectral Fluorescence Microscopy"],"prefix":"10.1002","volume":"19","author":[{"given":"Maciej","family":"Barna","sequence":"first","affiliation":[{"name":"Laser &amp; Fiber Electronics Group, Faculty of Electronics, Photonics and Microsystems Wroc\u0142aw University of Science and Technology  Wroc\u0142aw 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