{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T23:36:57Z","timestamp":1778197017956,"version":"3.51.4"},"reference-count":19,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2023,3,28]],"date-time":"2023-03-28T00:00:00Z","timestamp":1679961600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,3,28]],"date-time":"2023-03-28T00:00:00Z","timestamp":1679961600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"name":"EPFL Lausanne"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>High-throughput and selective detection of organelles in immunofluorescence images is an important but demanding task in cell biology. The centriole organelle is critical for fundamental cellular processes, and its accurate detection is key for analysing centriole function in health and disease. Centriole detection in human tissue culture cells has been achieved typically by manual determination of organelle number per cell. However, manual cell scoring of centrioles has a low throughput and is not reproducible. Published semi-automated methods tally the centrosome surrounding centrioles and not centrioles themselves. Furthermore, such methods rely on hard-coded parameters or require a multichannel input for cross-correlation. Therefore, there is a need for developing an efficient and versatile pipeline for the automatic detection of centrioles in single channel immunofluorescence datasets.\n<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>We developed a deep-learning pipeline termed <jats:italic>CenFind<\/jats:italic> that automatically scores cells for centriole numbers in immunofluorescence images of human cells. <jats:italic>CenFind<\/jats:italic> relies on the multi-scale convolution neural network <jats:italic>SpotNet<\/jats:italic>, which allows the accurate detection of sparse and minute foci in high resolution images. We built a dataset using different experimental settings and used it to train the model and evaluate existing detection methods. The resulting average F<jats:sub>1<\/jats:sub>-score\u00a0achieved by\u00a0<jats:italic>CenFind<\/jats:italic> is\u2009&gt;\u200990% across the test set, demonstrating the robustness of\u00a0the pipeline. Moreover, using the <jats:italic>StarDist<\/jats:italic>-based nucleus detector, we link the centrioles and procentrioles detected with <jats:italic>CenFind<\/jats:italic> to the cell containing them, overall enabling automatic scoring of centriole numbers per cell.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusions<\/jats:title>\n                <jats:p>Efficient, accurate, channel-intrinsic and reproducible detection of centrioles is an important unmet need in the field. Existing methods are either not discriminative enough or focus on a fixed multi-channel input. To fill this methodological gap, we developed <jats:italic>CenFind<\/jats:italic>, a command line interface pipeline that automates cell scoring of centrioles, thereby enabling channel-intrinsic, accurate and reproducible detection across experimental modalities. Moreover, the modular nature of <jats:italic>CenFind<\/jats:italic> enables its integration in other pipelines. Overall, we anticipate <jats:italic>CenFind<\/jats:italic> to prove critical for accelerating discoveries in the field.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12859-023-05214-2","type":"journal-article","created":{"date-parts":[[2023,3,28]],"date-time":"2023-03-28T10:03:21Z","timestamp":1679997801000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["CenFind: a deep-learning pipeline for efficient centriole detection in microscopy datasets"],"prefix":"10.1186","volume":"24","author":[{"given":"L\u00e9o","family":"B\u00fcrgy","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Martin","family":"Weigert","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Georgios","family":"Hatzopoulos","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Matthias","family":"Minder","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Adrien","family":"Journ\u00e9","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sahand Jamal","family":"Rahi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pierre","family":"G\u00f6nczy","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,3,28]]},"reference":[{"issue":"4","key":"5214_CR1","doi-asserted-by":"publisher","first-page":"jcs228833","DOI":"10.1242\/jcs.228833","volume":"132","author":"P G\u00f6nczy","year":"2019","unstructured":"G\u00f6nczy P, Hatzopoulos GN. 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