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However, fast and accurate identification of particles in cryo-tomograms is challenging and represents a significant bottleneck in downstream processes such as subtomogram averaging. Here, we present\n                    <jats:italic>tomoCPT<\/jats:italic>\n                    (\n                    <jats:italic>Tomogram Centroid Prediction Tool<\/jats:italic>\n                    ), a transformer-based solution that reformulates particle detection as a centroid-prediction task using Gaussian labels. Our approach, which is built upon the SwinUNETR architecture, demonstrates superior performance compared with both conventional binary labelling strategies and template matching. We show that\n                    <jats:italic>tomoCPT<\/jats:italic>\n                    effectively generalizes to novel particle types through zero-shot inference and can be significantly enhanced through fine-tuning with limited data. The efficacy of\n                    <jats:italic>tomoCPT<\/jats:italic>\n                    is validated using three case studies: apoferritin, achieving a resolution of 3.0\u2005\u00c5 compared with 3.3\u2005\u00c5 using template matching, SARS-CoV-2 spike proteins on cell surfaces, yielding an 18.3\u2005\u00c5 resolution map where template matching proved unsuccessful, and rubisco molecules within carboxysomes, reaching 8.0\u2005\u00c5 resolution. These results demonstrate the ability of\n                    <jats:italic>tomoCPT<\/jats:italic>\n                    to handle varied scenarios, including densely packed environments and membrane-bound proteins. The implementation of the tool as a command-line program, coupled with its minimal data requirements for fine-tuning, makes it a practical solution for high-throughput cryo-ET data-processing workflows.\n                  <\/jats:p>","DOI":"10.1107\/s2059798325000865","type":"journal-article","created":{"date-parts":[[2025,1,31]],"date-time":"2025-01-31T12:02:51Z","timestamp":1738324971000},"page":"63-76","update-policy":"https:\/\/doi.org\/10.1107\/cm_01","source":"Crossref","is-referenced-by-count":8,"title":["<i>TomoCPT<\/i>\n                    : a generalizable model for 3D particle detection and localization in cryo-electron tomograms"],"prefix":"10.1107","volume":"81","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1212-904X","authenticated-orcid":true,"given":"Pranav N. 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M. Shah\n                      et al.","order":0,"name":"copyright","label":"Copyright","explanation":{"URL":"https:\/\/journals.iucr.org\/services\/copyrightpolicy.html"},"group":{"name":"copyright_and_licencing","label":"Copyright and licencing"}},{"value":"Creative Commons Attribution (CC-BY)","order":1,"name":"license","label":"Licence","explanation":{"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/legalcode"},"group":{"name":"copyright_and_licencing","label":"Copyright and licencing"}}]}}