{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T15:33:01Z","timestamp":1772119981727,"version":"3.50.1"},"reference-count":28,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T00:00:00Z","timestamp":1722988800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,8,7]],"date-time":"2024-08-07T00:00:00Z","timestamp":1722988800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neuroinform"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Annotation of multiple regions of interest across the whole mouse brain is an indispensable process for quantitative evaluation of a multitude of study endpoints\u00a0in neuroscience digital pathology. Prior experience and domain expert knowledge are the key aspects for image annotation quality and consistency. At present, image annotation is often achieved manually by certified pathologists or trained technicians, limiting the total throughput of studies performed at neuroscience digital pathology labs. It may also mean that simpler and quicker methods of examining tissue samples are used by non-pathologists, especially in the early stages of research and preclinical studies. To address these limitations and to meet the growing demand for image analysis in a pharmaceutical setting, we developed AnNoBrainer, an open-source software tool that leverages deep learning, image registration, and standard cortical brain templates to automatically annotate individual brain regions on 2D pathology slides. Application of AnNoBrainer to a published set of pathology slides from transgenic mice models of synucleinopathy revealed comparable accuracy, increased reproducibility, and a significant reduction (~\u200950%) in time spent on brain annotation, quality control and labelling compared to trained scientists in pathology. Taken together, AnNoBrainer offers a rapid, accurate, and reproducible automated annotation of mouse brain images that largely meets the experts\u2019 histopathological assessment standards (&gt;\u200985% of cases) and enables high-throughput image analysis workflows in digital pathology labs.<\/jats:p>","DOI":"10.1007\/s12021-024-09679-1","type":"journal-article","created":{"date-parts":[[2024,8,6]],"date-time":"2024-08-06T23:32:42Z","timestamp":1722987162000},"page":"719-730","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["AnNoBrainer, An Automated Annotation of Mouse Brain Images using Deep Learning"],"prefix":"10.1007","volume":"22","author":[{"given":"Roman","family":"Peter","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Petr","family":"Hrobar","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Josef","family":"Navratil","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Martin","family":"Vagenknecht","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jindrich","family":"Soukup","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Keiko","family":"Tsuji","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nestor X.","family":"Barrezueta","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anna C.","family":"Stoll","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Renee C.","family":"Gentzel","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jonathan A.","family":"Sugam","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jacob","family":"Marcus","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Danny A.","family":"Bitton","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,8,7]]},"reference":[{"key":"9679_CR1","doi-asserted-by":"crossref","unstructured":"Aberman, K., Liao, J., Shi, M., Lischinski, D., Chen, B., & Cohen-Or, D. 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Temporal and regional progression of Alzheimer\u2019s disease\u2010like pathology in 3xTg\u2010AD mice.\u00a0Aging Cell, 18(1). https:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC6351836\/","DOI":"10.1111\/acel.12873"},{"key":"9679_CR4","doi-asserted-by":"crossref","unstructured":"Buslaev, A., Iglovikov, V. I., Khvedchenya, E., Parinov, A., Druzhinin, M., & Kalinin, A. A. (2020). Albumentations: fast and flexible image augmentations.\u00a0Information, 11(2), 125. http:\/\/arxiv.org\/abs\/1809.06839","DOI":"10.3390\/info11020125"},{"key":"9679_CR5","doi-asserted-by":"crossref","unstructured":"Carey, H. et al. (2023). DeepSlice: rapid fully automatic registration of mouse brain imaging to a volumetric atlas.","DOI":"10.1101\/2022.04.28.489953"},{"key":"9679_CR6","doi-asserted-by":"crossref","unstructured":"Carrigan, A. J., Charlton, A., Wiggins, M. W., Georgiou, A., Palmeri, T., & Curby, K. M. (2022). 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Neuronal Cell Death.\u00a0Physiological Reviews, 98(2), 813\u2013880. https:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC5966715\/","DOI":"10.1152\/physrev.00011.2017"},{"key":"9679_CR10","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., & Girshick, R. (2017). Mask R-CNN.\u00a0https:\/\/arxiv.org\/abs\/1703.06870","DOI":"10.1109\/ICCV.2017.322"},{"key":"9679_CR11","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., & Sun, J. (2015). Deep Residual Learning for Image Recognition.\u00a0http:\/\/arxiv.org\/abs\/1512.03385","DOI":"10.1109\/CVPR.2016.90"},{"key":"9679_CR12","doi-asserted-by":"crossref","unstructured":"Kiwitz, K., Schiffer, C., Spitzer, H., Dickscheid, T., & Amunts, K. (2020). Deep learning networks reflect cytoarchitectonic features used in brain mapping.\u00a0Scientific Reports, 10(1), 22039. https:\/\/www.nature.com\/articles\/s41598-020-78638-y","DOI":"10.1038\/s41598-020-78638-y"},{"key":"9679_CR13","doi-asserted-by":"crossref","unstructured":"Kuhn, H. W. (2005). The Hungarian method for the assignment problem.\u00a0Naval Research Logistics, 7\u201321.","DOI":"10.1002\/nav.20053"},{"key":"9679_CR14","doi-asserted-by":"crossref","unstructured":"LaFerla, F. M., & Green, K. N. (2012). Animal models of alzheimer disease.\u00a0Cold Spring Harbor Perspectives in Medicine, 2(11). https:\/\/www.ncbi.nlm.nih.gov\/pmc\/articles\/PMC3543097\/","DOI":"10.1101\/cshperspect.a006320"},{"key":"9679_CR15","unstructured":"Lein, E. S., Hawrylycz, M. J., Ao, N., Ayres, M., Bensinger, A., Bernard, A., et al. (2007). 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Proceedings of the IEEE (institute of Electrical and Electronics Engineers Inc.), 109(1), 43\u201376.","journal-title":"Proceedings of the IEEE (institute of Electrical and Electronics Engineers Inc.)"}],"container-title":["Neuroinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12021-024-09679-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12021-024-09679-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12021-024-09679-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,20]],"date-time":"2024-11-20T03:52:48Z","timestamp":1732074768000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12021-024-09679-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,7]]},"references-count":28,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2024,10]]}},"alternative-id":["9679"],"URL":"https:\/\/doi.org\/10.1007\/s12021-024-09679-1","relation":{"has-preprint":[{"id-type":"doi","id":"10.1101\/2024.01.12.575415","asserted-by":"object"}]},"ISSN":["1559-0089"],"issn-type":[{"value":"1559-0089","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,7]]},"assertion":[{"value":"2 July 2024","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 August 2024","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All authors that are\/were employees of Merck Sharp & Dohme LLC, a subsidiary of Merck & Co., Inc., Rahway, NJ, USA and may hold stocks and\/or stock options in Merck & Co., Inc., Rahway, NJ, USA. A subset of manuscript authors are inventors on a patent related to this work (patent application number: 34187-56244). All of this work\/code is licensed under the MIT permissive free software license.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}]}}