{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,22]],"date-time":"2025-10-22T20:39:51Z","timestamp":1761165591849,"version":"build-2065373602"},"reference-count":37,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T00:00:00Z","timestamp":1761004800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100008769","name":"Julius-Maximilians-Universit\u00e4t W\u00fcrzburg","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100008769","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Bioinformatics"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>\n                      <jats:bold>Background<\/jats:bold>\n                    <\/jats:title>\n                    <jats:p>Vasculature is an essential part of all tissues and organs and is involved in a wide range of different diseases. However, available software for blood vessel image analysis is often limited: Some only process two-dimensional data, others lack batch processing, putting a time burden on the user, while still others require tightly defined culturing methods and experimental conditions. This highlights the need for software that has the ability to batch process three-dimensional image data and requires few and simple experimental preparation steps.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>\n                      <jats:bold>Results<\/jats:bold>\n                    <\/jats:title>\n                    <jats:p>We present VESNA, a Fiji (ImageJ) macro for automated segmentation and skeletonization of three-dimensional fluorescence images, enabling quantitative vascular network analysis. It requires only basic experimental preparation, making it highly adaptable to a wide range of possible applications across experimental goals and different tissue culturing methods. The macro\u2019s potential is demonstrated on a range of different image data sets, from organoids with varying sizes, network complexities, and growth conditions, to expanding to other 3D\u00a0tissue culturing methods, with an example of hydrogel-based cultures.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>\n                      <jats:bold>Conclusions<\/jats:bold>\n                    <\/jats:title>\n                    <jats:p>With its ability to process large amounts of 3D image data and its flexibility across experimental conditions, VESNA fulfills previously unmet needs in image processing of vascular structures and can be a valuable tool for a variety of experimental setups around three-dimensional vasculature, such as drug screening, research in tissue development and disease mechanisms.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1186\/s12859-025-06270-6","type":"journal-article","created":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T08:13:20Z","timestamp":1761034400000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["VESNA: an open-source tool for automated 3D vessel segmentation and network analysis"],"prefix":"10.1186","volume":"26","author":[{"given":"Magdalena","family":"Sch\u00fcttler","sequence":"first","affiliation":[]},{"given":"Leyla","family":"Do\u011fan","sequence":"additional","affiliation":[]},{"given":"Jana","family":"Kirchner","sequence":"additional","affiliation":[]},{"given":"S\u00fcleyman","family":"Erg\u00fcn","sequence":"additional","affiliation":[]},{"given":"Philipp","family":"W\u00f6rsd\u00f6rfer","sequence":"additional","affiliation":[]},{"given":"Sabine C.","family":"Fischer","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,10,21]]},"reference":[{"issue":"7740","key":"6270_CR1","doi-asserted-by":"publisher","first-page":"505","DOI":"10.1038\/s41586-018-0858-8","volume":"565","author":"RA Wimmer","year":"2019","unstructured":"Wimmer RA, Leopoldi A, Aichinger M, Wick N, Hantusch B, Novatchkova M, et al. Human blood vessel organoids as a model of diabetic vasculopathy. Nature. 2019;565(7740):505\u201310.","journal-title":"Nature"},{"issue":"12","key":"6270_CR2","doi-asserted-by":"publisher","first-page":"3477","DOI":"10.1007\/s00432-021-03814-0","volume":"147","author":"SM Bhat","year":"2021","unstructured":"Bhat SM, Badiger VA, Vasishta S, Chakraborty J, Prasad S, Ghosh S, et al. 3d tumor angiogenesis models: recent advances and challenges. J Cancer Res Clin Oncol. 2021;147(12):3477\u201394.","journal-title":"J Cancer Res Clin Oncol"},{"issue":"1","key":"6270_CR3","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1186\/s13045-022-01278-4","volume":"15","author":"X Hanxiao","year":"2022","unstructured":"Hanxiao X, Jiao D, Liu A, Kongming Wu. Tumor organoids: applications in cancer modeling and potentials in precision medicine. J Hematol Oncol. 2022;15(1):58.","journal-title":"J Hematol Oncol"},{"issue":"1","key":"6270_CR4","doi-asserted-by":"publisher","first-page":"32","DOI":"10.1038\/s41420-022-01288-8","volume":"9","author":"D Kong","year":"2023","unstructured":"Kong D, Park KH, Kim D-H, Kim NG, Lee S-E, Shin N, et al. Cortical-blood vessel assembloids exhibit Alzheimer\u2019s disease phenotypes by activating glia after SARS-CoV-2 infection. Cell Death Discovery. 2023;9(1):32.","journal-title":"Cell Death Discovery"},{"issue":"6","key":"6270_CR5","doi-asserted-by":"publisher","first-page":"818","DOI":"10.1016\/j.stem.2024.04.019","volume":"31","author":"L Dao","year":"2024","unstructured":"Dao L, You Z, Lu L, Xu T, Sarkar AK, Zhu H, et al. Modeling blood-brain barrier formation and cerebral cavernous malformations in human PSC-derived organoids. Cell Stem Cell. 2024;31(6):818\u201333.","journal-title":"Cell Stem Cell"},{"issue":"4","key":"6270_CR6","doi-asserted-by":"publisher","DOI":"10.1088\/1758-5090\/ad5ac0","volume":"16","author":"KT Kroll","year":"2024","unstructured":"Kroll KT, Homan KA, Uzel SGM, Mata MM, Wolf KJ, Rubins JE, et al. A perfusable, vascularized kidney organoid-on-chip model. Biofabrication. 2024;16(4):045003.","journal-title":"Biofabrication"},{"issue":"18","key":"6270_CR7","doi-asserted-by":"publisher","DOI":"10.1242\/dev.199611","volume":"148","author":"N Gritti","year":"2021","unstructured":"Gritti N, Lim JL, Anla\u015f K, Pandya M, Aalderink G, Mart\u00ednez-Ara G, et al. Morgana: accessible quantitative analysis of organoids with machine learning. Development. 2021;148(18):dev199611.","journal-title":"Development"},{"issue":"11","key":"6270_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pcbi.1010584","volume":"18","author":"JM Matthews","year":"2022","unstructured":"Matthews JM, Schuster B, Kashaf SS, Liu P, Ben-Yishay R, Ishay-Ronen D, et al. Organoid: a versatile deep learning platform for tracking and analysis of single-organoid dynamics. PLoS Comput Biol. 2022;18(11):1\u201316.","journal-title":"PLoS Comput Biol"},{"issue":"4","key":"6270_CR9","doi-asserted-by":"publisher","first-page":"549","DOI":"10.1109\/TMI.2004.837339","volume":"24","author":"A Niemisto","year":"2005","unstructured":"Niemisto A, Dunmire V, Yli-Harja O, Zhang W, Shmulevich I. Robust quantification of in vitro angiogenesis through image analysis. IEEE Trans Med Imaging. 2005;24(4):549\u201353.","journal-title":"IEEE Trans Med Imaging"},{"issue":"3","key":"6270_CR10","doi-asserted-by":"publisher","first-page":"320","DOI":"10.1002\/ar.20862","volume":"292","author":"MB Vickerman","year":"2009","unstructured":"Vickerman MB, Keith PA, McKay TL, Gedeon DJ, Watanabe M, Montano M, et al. VESGEN 2D: automated, user-interactive software for quantification and mapping of angiogenic and lymphangiogenic trees and networks. Anat Rec. 2009;292(3):320\u201332.","journal-title":"Anat Rec"},{"issue":"11","key":"6270_CR11","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0027385","volume":"6","author":"E Zudaire","year":"2011","unstructured":"Zudaire E, Gambardella L, Kurcz C, Vermeren S. A computational tool for quantitative analysis of vascular networks. PLoS ONE. 2011;6(11):e27385.","journal-title":"PLoS ONE"},{"key":"6270_CR12","doi-asserted-by":"publisher","first-page":"244","DOI":"10.3389\/fnins.2020.00244","volume":"14","author":"R Rust","year":"2020","unstructured":"Rust R, Kirabali T, Gr\u00f6nnert L, Dogancay B, Limasale YDP, Meinhardt A, et al. A practical guide to the automated analysis of vascular growth, maturation and injury in the brain. Front Neurosci. 2020;14:244.","journal-title":"Front Neurosci"},{"issue":"5","key":"6270_CR13","doi-asserted-by":"publisher","first-page":"e12618","DOI":"10.1111\/micc.12618","volume":"27","author":"BA Corliss","year":"2020","unstructured":"Corliss BA, Doty RW, Mathews C, Yates PA, Zhang T, Peirce SM. Reaver: a program for improved analysis of high-resolution vascular network images. Microcirculation. 2020;27(5):e12618.","journal-title":"Microcirculation"},{"issue":"1","key":"6270_CR14","doi-asserted-by":"publisher","first-page":"11568","DOI":"10.1038\/s41598-020-67289-8","volume":"10","author":"G Carpentier","year":"2020","unstructured":"Carpentier G, Berndt S, Ferratge S, Rasband W, Cuendet M, Uzan G, et al. Angiogenesis analyzer for imagej\u2014a comparative morphometric analysis of \u201cendothelial tube formation assay\u2019\u2019 and \u201cfibrin bead assay\u2019\u2019. Sci Rep. 2020;10(1):11568.","journal-title":"Sci Rep"},{"key":"6270_CR15","doi-asserted-by":"publisher","first-page":"1147462","DOI":"10.3389\/fcvm.2023.1147462","volume":"10","author":"B Callewaert","year":"2023","unstructured":"Callewaert B, Gsell W, Himmelreich U, Jones EAV. Q-VAT: quantitative vascular analysis tool. Front Cardiovasc Med. 2023;10:1147462.","journal-title":"Front Cardiovasc Med"},{"issue":"1","key":"6270_CR16","doi-asserted-by":"publisher","first-page":"24909","DOI":"10.1038\/s41598-023-33090-6","volume":"13","author":"M Beter","year":"2023","unstructured":"Beter M, Abdollahzadeh A, Pulkkinen HH, Huang H, Orsenigo F, Magnusson PU, et al. SproutAngio: an open-source bioimage informatics tool for quantitative analysis of sprouting angiogenesis and lumen space. Sci Rep. 2023;13(1):24909\u201328.","journal-title":"Sci Rep"},{"issue":"3","key":"6270_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.crmeth.2023.100436","volume":"3","author":"P Spangenberg","year":"2023","unstructured":"...Spangenberg P, Hagemann N, Squire A, F\u00f6rster N, Krau\u00df SD, Qi Y, et al. Rapid and fully automated blood vasculature analysis in 3D light-sheet image volumes of different organs. Cell Rep Methods. 2023;3(3):100436.","journal-title":"Cell Rep Methods"},{"issue":"7","key":"6270_CR18","doi-asserted-by":"publisher","first-page":"676","DOI":"10.1038\/nmeth.2019","volume":"9","author":"J Schindelin","year":"2012","unstructured":"Schindelin J, Arganda-Carreras I, Frise E, Kaynig V, Longair M, Pietzsch T, et al. Fiji: an open-source platform for biological-image analysis. Nat Methods. 2012;9(7):676\u201382.","journal-title":"Nat Methods"},{"issue":"3","key":"6270_CR19","doi-asserted-by":"publisher","first-page":"370","DOI":"10.1109\/83.366472","volume":"4","author":"JC Yen","year":"1995","unstructured":"Yen JC, Chang FJ, Chang S. A new criterion for automatic multilevel thresholding. IEEE Trans Image Process. 1995;4(3):370\u20138.","journal-title":"IEEE Trans Image Process"},{"issue":"22","key":"6270_CR20","doi-asserted-by":"publisher","first-page":"3532","DOI":"10.1093\/bioinformatics\/btw413","volume":"32","author":"D Legland","year":"2016","unstructured":"Legland D, Arganda-Carreras I, Andrey P. MorphoLibJ: integrated library and plugins for mathematical morphology with ImageJ. Bioinformatics. 2016;32(22):3532\u20134.","journal-title":"Bioinformatics"},{"issue":"15","key":"6270_CR21","doi-asserted-by":"publisher","first-page":"2424","DOI":"10.1093\/bioinformatics\/btx180","volume":"33","author":"I Arganda-Carreras","year":"2017","unstructured":"Arganda-Carreras I, Kaynig V, Rueden C, Eliceiri KW, Schindelin J, Cardona A, et al. Trainable weka segmentation: a machine learning tool for microscopy pixel classification. Bioinformatics. 2017;33(15):2424\u20136.","journal-title":"Bioinformatics"},{"key":"6270_CR22","unstructured":"Arganda-Carreras I. Skeletonize3d, 2008."},{"key":"6270_CR23","unstructured":"Burri O. Prune skeleton ends, 2017."},{"issue":"11","key":"6270_CR24","doi-asserted-by":"publisher","first-page":"1019","DOI":"10.1002\/jemt.20829","volume":"73","author":"I Arganda-Carreras","year":"2010","unstructured":"Arganda-Carreras I, Fern\u00e1ndez-Gonz\u00e1lez R, Mu\u00f1oz-Barrutia A, Ortiz-De-Solorzano C. 3d reconstruction of histological sections: application to mammary gland tissue. Microsc Res Tech. 2010;73(11):1019\u201329.","journal-title":"Microsc Res Tech"},{"issue":"3","key":"6270_CR25","doi-asserted-by":"publisher","first-page":"543","DOI":"10.1634\/stemcells.2008-1075","volume":"27","author":"CA Sommer","year":"2009","unstructured":"Sommer CA, Stadtfeld M, Murphy GJ, Hochedlinger K, Kotton DN, Mostoslavsky G. Induced pluripotent stem cell generation using a single lentiviral stem cell cassette. Stem Cells. 2009;27(3):543\u20139.","journal-title":"Stem Cells"},{"issue":"2","key":"6270_CR26","first-page":"e1076","volume":"12","author":"CK Kwok","year":"2017","unstructured":"Kwok CK, Ueda Y, Kadari A, G\u00fcnther K, Erg\u00fcn S, Heron A, et al. Scalable stirred suspension culture for the generation of billions of human induced pluripotent stem cells using single-use bioreactors. J Tissue Eng Reg Med. 2017;12(2):e1076\u201387.","journal-title":"J Tissue Eng Reg Med"},{"issue":"1","key":"6270_CR27","doi-asserted-by":"publisher","first-page":"41","DOI":"10.3390\/organoids1010005","volume":"1","author":"S Schmidt","year":"2022","unstructured":"Schmidt S, Alt Y, Deoghare N, Kr\u00fcger S, Kern A, Rockel AF, et al. A blood vessel organoid model recapitulating aspects of vasculogenesis, angiogenesis and vessel wall maturation. Organoids. 2022;1(1):41\u201353.","journal-title":"Organoids"},{"key":"6270_CR28","doi-asserted-by":"crossref","unstructured":"W\u00f6rsd\u00f6rfer P, Rockel AF, Alt Y, Kern A, Erg\u00fcn S. Generation of vascularized neural organoids by co-culturing with mesodermal progenitor cells. STAR Protoc. 2020;1(1)100041.","DOI":"10.1016\/j.xpro.2020.100041"},{"issue":"4","key":"6270_CR29","doi-asserted-by":"publisher","DOI":"10.1088\/1758-5090\/ac26ac","volume":"13","author":"L Dogan","year":"2021","unstructured":"Dogan L, Scheuring R, Wagner N, Ueda Y, Schmidt S, W\u00f6rsd\u00f6rfer P, et al. Human iPSC-derived mesodermal progenitor cells preserve their vasculogenesis potential after extrusion and form hierarchically organized blood vessels. Biofabrication. 2021;13(4):045028.","journal-title":"Biofabrication"},{"key":"6270_CR30","doi-asserted-by":"crossref","unstructured":"W\u00f6rsd\u00f6rfer P, Dalda N, Kern A, et al. Generation of complex human organoid models including vascular networks by incorporating mesodermal progenitor cells. Sci Rep. 2019;9(1).","DOI":"10.1038\/s41598-019-52204-7"},{"key":"6270_CR31","volume-title":"Statistical power analysis for the behavioral sciences","author":"J Cohen","year":"1988","unstructured":"Cohen J. Statistical power analysis for the behavioral sciences. New Jersey: Lawrence Erlbaum Associates; 1988."},{"key":"6270_CR32","doi-asserted-by":"publisher","first-page":"829","DOI":"10.2147\/DDDT.S443107","volume":"18","author":"J Li","year":"2024","unstructured":"Li J, Zhang L, Ge T, Liu J, Wang C, Qi Yu. Understanding sorafenib-induced cardiovascular toxicity: mechanisms and treatment implications. Drug Des Devel Ther. 2024;18:829\u201343.","journal-title":"Drug Des Devel Ther"},{"issue":"552","key":"6270_CR33","doi-asserted-by":"publisher","first-page":"eaau1165","DOI":"10.1126\/scisignal.aau1165","volume":"11","author":"AL Elaimy","year":"2018","unstructured":"Elaimy AL, Mercurio AM. Convergence of VEGF and YAP\/TAZ signaling: implications for angiogenesis and cancer biology. Sci Signal. 2018;11(552):eaau1165.","journal-title":"Sci Signal"},{"key":"6270_CR34","unstructured":"US\u00a0Food I & Drug\u00a0Administration (FDA). Drug approval package: Nexavar (sorafenib), 2006."},{"key":"6270_CR35","unstructured":"European Medicines\u00a0Agency (EMA). Nexavar, 2024."},{"issue":"Supplement","key":"6270_CR36","first-page":"S84","volume":"16","author":"M Kumar","year":"2020","unstructured":"Kumar M, Meshram GG, Rastogi T, Sharma S, Gupta R, Jain S, et al. Antiangiogenic activity of zinc and zinc-sorafenib combination using the chick chorioallantoic membrane assay: a descriptive study. J Cancer Res Ther. 2020;16(Supplement):S84\u20139.","journal-title":"J Cancer Res Ther"},{"issue":"2","key":"6270_CR37","doi-asserted-by":"publisher","first-page":"484","DOI":"10.1364\/BOE.8.000484","volume":"8","author":"I Smyrek","year":"2017","unstructured":"Smyrek I, Stelzer EHK. Quantitative three-dimensional evaluation of immunofluorescence staining for large whole mount spheroids with light sheet microscopy. Biomed Opt Express. 2017;8(2):484\u201399.","journal-title":"Biomed Opt Express"}],"container-title":["BMC Bioinformatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-025-06270-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s12859-025-06270-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s12859-025-06270-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,21]],"date-time":"2025-10-21T20:03:13Z","timestamp":1761076993000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcbioinformatics.biomedcentral.com\/articles\/10.1186\/s12859-025-06270-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,21]]},"references-count":37,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["6270"],"URL":"https:\/\/doi.org\/10.1186\/s12859-025-06270-6","relation":{},"ISSN":["1471-2105"],"issn-type":[{"type":"electronic","value":"1471-2105"}],"subject":[],"published":{"date-parts":[[2025,10,21]]},"assertion":[{"value":"14 March 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 September 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 October 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval and consent to participate"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"254"}}