{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/automatic-detection-of-neurons-in-neun","title":"Automatic Detection of Neurons in NeuN-stained Histological Images of Human Brain","arxiv_id":"1806.00292","date":"2018-06-01","proceeding":null,"authors":["Andrija Štajduhar","Domagoj Džaja","Miloš Judaš","Sven Lončarić"],"abstract":"In this paper, we present a novel use of an anisotropic diffusion model for\nautomatic detection of neurons in histological sections of the adult human\nbrain cortex. We use a partial differential equation model to process high\nresolution images to acquire locations of neuronal bodies. We also present a\nnovel approach in model training and evaluation that considers variability\namong the human experts, addressing the issue of existence and correctness of\nthe golden standard for neuron and cell counting, used in most of relevant\npapers. Our method, trained on dataset manually labeled by three experts, has\ncorrectly distinguished over 95% of neuron bodies in test data, doing so in\ntime much shorter than other comparable methods.","url_abs":"http://arxiv.org/abs/1806.00292v1","url_pdf":"http://arxiv.org/pdf/1806.00292v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"automatic-detection-of-neurons-in-neun","repo_url":"https://github.com/astajd/neurons","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}