{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T16:52:50Z","timestamp":1779382370071,"version":"3.53.1"},"update-to":[{"DOI":"10.1371\/journal.pcbi.1012349","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2025,2,5]],"date-time":"2025-02-05T00:00:00Z","timestamp":1738713600000}}],"reference-count":31,"publisher":"Public Library of Science (PLoS)","issue":"1","license":[{"start":{"date-parts":[[2025,1,29]],"date-time":"2025-01-29T00:00:00Z","timestamp":1738108800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100013060","name":"European Molecular Biology Laboratory","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100013060","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100013060","name":"European Molecular Biology Laboratory","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100013060","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100013060","name":"European Molecular Biology Laboratory","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100013060","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001659","name":"Deutsche Forschungsgemeinschaft","doi-asserted-by":"publisher","award":["452616889"],"award-info":[{"award-number":["452616889"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Eindhoven, Wageningen Utrecht Alliance through the Centre for Living Technologies"},{"name":"Eindhoven, Wageningen Utrecht Alliance through the Centre for Living Technologies"},{"name":"Eindhoven, Wageningen Utrecht Alliance through the Centre for Living Technologies"}],"content-domain":{"domain":["www.ploscompbiol.org"],"crossmark-restriction":false},"short-container-title":["PLoS Comput Biol"],"abstract":"<jats:p>\n                    The characterization of phenotypes in cells or organisms from microscopy data largely depends on differences in the spatial distribution of image intensity. Multiple methods exist for quantifying the intensity distribution - or image texture - across objects in natural images. However, many of these texture extraction methods do not directly adapt to 3D microscopy data. Here, we present\n                    <jats:italic>Spherical Texture<\/jats:italic>\n                    extraction, which measures the variance in intensity per angular wavelength by calculating the Spherical Harmonics or Fourier power spectrum of a spherical or circular projection of the angular mean intensity of the object. This method provides a 20-value characterization that quantifies the scale of features in the spherical projection of the intensity distribution, giving a different signal if the intensity is, for example, clustered in parts of the volume or spread across the entire volume. We apply this method to different systems and demonstrate its ability to describe various biological problems through feature extraction. The\n                    <jats:italic>Spherical Texture<\/jats:italic>\n                    extraction characterizes biologically defined gene expression patterns in\n                    <jats:italic>Drosophila melanogaster<\/jats:italic>\n                    embryos, giving a quantitative read-out for pattern formation. Our method can also quantify morphological differences in\n                    <jats:italic>Caenorhabditis elegans<\/jats:italic>\n                    germline nuclei, which lack a predefined pattern. We show that the classification of germline nuclei using their\n                    <jats:italic>Spherical Texture<\/jats:italic>\n                    outperforms a convolutional neural net when training data is limited. Additionally, we use a similar pipeline on 2D cell migration data to extract the polarization direction and quantify the alignment of fluorescent markers to the migration direction. We implemented the\n                    <jats:italic>Spherical Texture<\/jats:italic>\n                    method as a plugin in\n                    <jats:italic>ilastik<\/jats:italic>\n                    to provide a parameter-free and data-agnostic application to any segmented 3D or 2D dataset. Additionally, this technique can also be applied through a Python package to provide extra feature extraction for any object classification pipeline or downstream analysis.\n                  <\/jats:p>","DOI":"10.1371\/journal.pcbi.1012349","type":"journal-article","created":{"date-parts":[[2025,1,29]],"date-time":"2025-01-29T13:35:33Z","timestamp":1738157733000},"page":"e1012349","update-policy":"https:\/\/doi.org\/10.1371\/journal.pcbi.corrections_policy","source":"Crossref","is-referenced-by-count":1,"title":["Spherical harmonics texture extraction for versatile analysis of biological objects"],"prefix":"10.1371","volume":"21","author":[{"given":"Oane","family":"Gros","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Josiah B.","family":"Passmore","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0445-6761","authenticated-orcid":true,"given":"Noa O.","family":"Borst","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dominik","family":"Kutra","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wilco","family":"Nijenhuis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Timothy","family":"Fuqua","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lukas C.","family":"Kapitein","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Justin M.","family":"Crocker","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anna","family":"Kreshuk","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1119-8855","authenticated-orcid":true,"given":"Simone","family":"K\u00f6hler","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2025,1,29]]},"reference":[{"issue":"12","key":"pcbi.1012349.ref001","doi-asserted-by":"crossref","first-page":"e57739","DOI":"10.15252\/embr.202357739","article-title":"Forceful patterning: theoretical principles of mechanochemical pattern formation","volume":"24","author":"J Rombouts","year":"2023","journal-title":"EMBO Rep"},{"key":"pcbi.1012349.ref002","author":"L Armi","year":"2019"},{"key":"pcbi.1012349.ref003","doi-asserted-by":"crossref","first-page":"8975","DOI":"10.1109\/ACCESS.2018.2890743","article-title":"Texture feature extraction methods: a survey","volume":"7","author":"A Humeau-Heurtier","year":"2019","journal-title":"IEEE Access"},{"issue":"12","key":"pcbi.1012349.ref004","doi-asserted-by":"crossref","first-page":"1226","DOI":"10.1038\/s41592-019-0582-9","article-title":"Ilastik: interactive machine learning for (bio)image analysis","volume":"16","author":"S Berg","year":"2019","journal-title":"Nat Methods"},{"issue":"1","key":"pcbi.1012349.ref005","doi-asserted-by":"crossref","first-page":"100","DOI":"10.1038\/s41592-020-01018-x","article-title":"Cellpose: a generalist algorithm for cellular segmentation","volume":"18","author":"C Stringer","year":"2021","journal-title":"Nat Methods"},{"key":"pcbi.1012349.ref006","first-page":"3655","volume-title":"Star-convex Polyhedra for 3D Object Detection and Segmentation in Microscopy. 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