{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,8]],"date-time":"2025-12-08T22:38:47Z","timestamp":1765233527978,"version":"build-2065373602"},"reference-count":90,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2023,5,9]],"date-time":"2023-05-09T00:00:00Z","timestamp":1683590400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"European Union\u2019s Horizon 2020 Framework Programme for Research and Innovation","award":["945539","SGA3","IR0000011","CUP B51E22000150006","CUP G69J18001100007","08CT4669990220","PE_00000019","CUP B73C22001250006"],"award-info":[{"award-number":["945539","SGA3","IR0000011","CUP B51E22000150006","CUP G69J18001100007","08CT4669990220","PE_00000019","CUP B73C22001250006"]}]},{"name":"European Union\u2013NextGenerationEU","award":["945539","SGA3","IR0000011","CUP B51E22000150006","CUP G69J18001100007","08CT4669990220","PE_00000019","CUP B73C22001250006"],"award-info":[{"award-number":["945539","SGA3","IR0000011","CUP B51E22000150006","CUP G69J18001100007","08CT4669990220","PE_00000019","CUP B73C22001250006"]}]},{"name":"PO FESR Sicilia 2014\/2020, Azione 1.1.5","award":["945539","SGA3","IR0000011","CUP B51E22000150006","CUP G69J18001100007","08CT4669990220","PE_00000019","CUP B73C22001250006"],"award-info":[{"award-number":["945539","SGA3","IR0000011","CUP B51E22000150006","CUP G69J18001100007","08CT4669990220","PE_00000019","CUP B73C22001250006"]}]},{"name":"Italian Ministry of University and Research","award":["945539","SGA3","IR0000011","CUP B51E22000150006","CUP G69J18001100007","08CT4669990220","PE_00000019","CUP B73C22001250006"],"award-info":[{"award-number":["945539","SGA3","IR0000011","CUP B51E22000150006","CUP G69J18001100007","08CT4669990220","PE_00000019","CUP B73C22001250006"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Background: Image analysis applications in digital pathology include various methods for segmenting regions of interest. Their identification is one of the most complex steps and therefore of great interest for the study of robust methods that do not necessarily rely on a machine learning (ML) approach. Method: A fully automatic and optimized segmentation process for different datasets is a prerequisite for classifying and diagnosing indirect immunofluorescence (IIF) raw data. This study describes a deterministic computational neuroscience approach for identifying cells and nuclei. It is very different from the conventional neural network approaches but has an equivalent quantitative and qualitative performance, and it is also robust against adversative noise. The method is robust, based on formally correct functions, and does not suffer from having to be tuned on specific data sets. Results: This work demonstrates the robustness of the method against variability of parameters, such as image size, mode, and signal-to-noise ratio. We validated the method on three datasets (Neuroblastoma, NucleusSegData, and ISBI 2009 Dataset) using images annotated by independent medical doctors. Conclusions: The definition of deterministic and formally correct methods, from a functional and structural point of view, guarantees the achievement of optimized and functionally correct results. The excellent performance of our deterministic method (NeuronalAlg) in segmenting cells and nuclei from fluorescence images was measured with quantitative indicators and compared with those achieved by three published ML approaches.<\/jats:p>","DOI":"10.3390\/s23104598","type":"journal-article","created":{"date-parts":[[2023,5,10]],"date-time":"2023-05-10T01:57:51Z","timestamp":1683683871000},"page":"4598","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["NeuronAlg: An Innovative Neuronal Computational Model for Immunofluorescence Image Segmentation"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6801-8811","authenticated-orcid":false,"given":"Giuseppe","family":"Giacopelli","sequence":"first","affiliation":[{"name":"National Research Council, Institute of Biophysics, 90153 Palermo, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7584-6292","authenticated-orcid":false,"given":"Michele","family":"Migliore","sequence":"additional","affiliation":[{"name":"National Research Council, Institute of Biophysics, 90153 Palermo, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5417-5584","authenticated-orcid":false,"given":"Domenico","family":"Tegolo","sequence":"additional","affiliation":[{"name":"National Research Council, Institute of Biophysics, 90153 Palermo, Italy"},{"name":"Dipartimento Matematica e Informatica, Universit\u00e1 degli Studi di Palermo, 90123 Palermo, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1109\/RBME.2009.2034865","article-title":"Histopathological Image Analysis: A Review","volume":"2","author":"Gurcan","year":"2009","journal-title":"IEEE Rev. 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