{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T17:19:26Z","timestamp":1783185566213,"version":"3.54.6"},"reference-count":32,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100005632","name":"National Centre for Research and Development","doi-asserted-by":"publisher","award":["ENM3\/IV\/18\/RXnanoBRAIN\/2022"],"award-info":[{"award-number":["ENM3\/IV\/18\/RXnanoBRAIN\/2022"]}],"id":[{"id":"10.13039\/501100005632","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004281","name":"Narodowe Centrum Nauki","doi-asserted-by":"publisher","award":["2022\/45\/B\/NZ5\/01695"],"award-info":[{"award-number":["2022\/45\/B\/NZ5\/01695"]}],"id":[{"id":"10.13039\/501100004281","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004281","name":"Narodowe Centrum Nauki","doi-asserted-by":"publisher","award":["NCN 2022\/45\/B\/NZ4\/01215"],"award-info":[{"award-number":["NCN 2022\/45\/B\/NZ4\/01215"]}],"id":[{"id":"10.13039\/501100004281","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computer Methods and Programs in Biomedicine"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1016\/j.cmpb.2026.109403","type":"journal-article","created":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T06:52:43Z","timestamp":1776840763000},"page":"109403","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["From 2D to 3D: Automated ultrasound segmentation and cross-sectional validation in murine tumor models"],"prefix":"10.1016","volume":"282","author":[{"given":"Weronika","family":"Smolak-Dy\u017cewska","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jerzy","family":"Bazak","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-6693-4496","authenticated-orcid":false,"given":"Wiktoria","family":"Brandys","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aleksandra","family":"Bienia","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aleksandra","family":"Murzyn","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-9228-1854","authenticated-orcid":false,"given":"Bartosz","family":"P\u0142\u00f3ciennik","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1968-2238","authenticated-orcid":false,"given":"Gniewosz","family":"Drwi\u0119ga","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Julia","family":"Kozik","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8652-1370","authenticated-orcid":false,"given":"Agnieszka","family":"Drza\u0142","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bartosz","family":"Leszczy\u0144ski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0097-5521","authenticated-orcid":false,"given":"Przemys\u0142aw","family":"Spurek","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6296-2844","authenticated-orcid":false,"given":"Martyna","family":"Elas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2038-8105","authenticated-orcid":false,"given":"Martyna","family":"Krzykawska-Serda","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.cmpb.2026.109403_bib0001","doi-asserted-by":"crossref","DOI":"10.3389\/fphy.2020.00124","article-title":"Preclinical ultrasound imaging\u2014A review of techniques and imaging applications","volume":"8","author":"Moran","year":"2020","journal-title":"Front. Phys."},{"key":"10.1016\/j.cmpb.2026.109403_bib0002","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0307558","article-title":"Evaluation of superficial xenograft volume estimation by ultrasound and caliper against MRI in a longitudinal pre-clinical radiotherapeutic setting","volume":"19","author":"Roth","year":"2024","journal-title":"PLoS. One"},{"key":"10.1016\/j.cmpb.2026.109403_bib0003","doi-asserted-by":"crossref","DOI":"10.1038\/s41467-018-03973-8","article-title":"Motion model ultrasound localization microscopy for preclinical and clinical multiparametric tumor characterization","volume":"9","author":"Opacic","year":"2018","journal-title":"Nat. Commun."},{"key":"10.1016\/j.cmpb.2026.109403_bib0004","doi-asserted-by":"crossref","first-page":"1910","DOI":"10.1016\/j.ultrasmedbio.2018.03.015","article-title":"Semi-automated segmentation of the tumor vasculature in contrast-enhanced ultrasound data","volume":"44","author":"Theek","year":"2018","journal-title":"Ultrasound. Med. Biol."},{"key":"10.1016\/j.cmpb.2026.109403_bib0005","series-title":"BUSIS: A Benchmark For Breast Ultrasound Image Segmentation","first-page":"10","author":"Zhang","year":"2022"},{"key":"10.1016\/j.cmpb.2026.109403_bib0006","doi-asserted-by":"crossref","DOI":"10.1002\/acm2.13863","article-title":"Fully automatic tumor segmentation of breast ultrasound images with deep learning","volume":"24","author":"Zhang","year":"2023","journal-title":"J. Appl. Clin. Med. Phys."},{"key":"10.1016\/j.cmpb.2026.109403_bib0007","doi-asserted-by":"crossref","DOI":"10.3390\/diagnostics11091565","article-title":"Tumor segmentation in breast ultrasound image by means of res path combined with dense connection neural network","volume":"11","author":"Yu","year":"2021","journal-title":"Diagnostics"},{"key":"10.1016\/j.cmpb.2026.109403_bib0008","first-page":"1","article-title":"A convolutional attention model for predicting response to chemo-immunotherapy from ultrasound elastography in mouse tumor models","author":"Voutouri","year":"2024","journal-title":"Commun. Med. L"},{"key":"10.1016\/j.cmpb.2026.109403_bib0009","doi-asserted-by":"crossref","first-page":"1759","DOI":"10.3390\/life13081759","article-title":"Applications of deep learning algorithms to ultrasound imaging analysis in preclinical studies on In vivo animals","volume":"13","author":"De Rosa","year":"2023","journal-title":"Life"},{"key":"10.1016\/j.cmpb.2026.109403_bib0010","doi-asserted-by":"crossref","first-page":"604","DOI":"10.1016\/j.media.2007.05.002","article-title":"Probe trajectory interpolation for 3D reconstruction of freehand ultrasound","volume":"11","author":"Coup\u00e9","year":"2007","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.cmpb.2026.109403_bib0011","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.echo.2014.10.003","article-title":"Recommendations for cardiac chamber quantification by echocardiography in adults: an update from the American Society of Echocardiography and the European Association of Cardiovascular Imaging","volume":"28","author":"Lang","year":"2015","journal-title":"J. Am. Soc. Echocardiogr."},{"key":"10.1016\/j.cmpb.2026.109403_bib0012","doi-asserted-by":"crossref","first-page":"2099","DOI":"10.1016\/j.ultrasmedbio.2017.06.009","article-title":"Freehand 3-D ultrasound Imaging: a systematic review","volume":"43","author":"Mozaffari","year":"2017","journal-title":"Ultrasound. Med. Biol."},{"key":"10.1016\/j.cmpb.2026.109403_bib0013","doi-asserted-by":"crossref","first-page":"970","DOI":"10.1109\/TBME.2022.3206596","article-title":"Ultrasound volume reconstruction from freehand scans without tracking HHS public access","volume":"70","author":"Guo","year":"2023","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"10.1016\/j.cmpb.2026.109403_bib0014","doi-asserted-by":"crossref","DOI":"10.1186\/1475-925X-13-124","article-title":"Reconstruction of freehand 3D ultrasound based on kernel regression","volume":"13","author":"Chen","year":"2014","journal-title":"Biomed. Eng. Online"},{"key":"10.1016\/j.cmpb.2026.109403_bib0015","unstructured":"P.Y. Simard, D. Steinkraus, J.C. Platt, Best practices for convolutional neural networks applied to visual document analysis, (2003)."},{"key":"10.1016\/j.cmpb.2026.109403_bib0016","series-title":"Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics","first-page":"234","article-title":"U-Net: convolutional networks for biomedical image segmentation","volume":"9351","author":"Ronneberger","year":"2015"},{"key":"10.1016\/j.cmpb.2026.109403_bib0017","doi-asserted-by":"crossref","first-page":"749","DOI":"10.1109\/LGRS.2018.2802944","article-title":"Road extraction by deep residual U-net","volume":"15","author":"Zhang","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"10.1016\/j.cmpb.2026.109403_bib0018","series-title":"Attention U-Net: learning where to look for the Pancreas","author":"Oktay","year":"2018"},{"key":"10.1016\/j.cmpb.2026.109403_bib0019","series-title":"R2AU-Net: Attention Recurrent Residual Convolutional Neural Network For Multimodal Medical Image Segmentation","author":"Zuo","year":"2021"},{"key":"10.1016\/j.cmpb.2026.109403_bib0020","doi-asserted-by":"crossref","DOI":"10.7717\/peerj-cs.2226","article-title":"AI-based automated breast cancer segmentation in ultrasound imaging based on attention gated multi ResU-Net","volume":"10","author":"Ding","year":"2024","journal-title":"PeerJ. Comput. Sci."},{"key":"10.1016\/j.cmpb.2026.109403_bib0021","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1038\/s41523-021-00358-x","article-title":"Connected-UNets: a deep learning architecture for breast mass segmentation","volume":"7","author":"Baccouche","year":"2021","journal-title":"NPJ. Breast. Cancer"},{"key":"10.1016\/j.cmpb.2026.109403_bib0022","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-024-72712-5","article-title":"Attention based UNet model for breast cancer segmentation using BUSI dataset","volume":"14","author":"Sulaiman","year":"2024","journal-title":"Sci. Rep."},{"key":"10.1016\/j.cmpb.2026.109403_bib0027","doi-asserted-by":"crossref","first-page":"3223","DOI":"10.1002\/mp.16287","article-title":"BUS-Set: a benchmark for quantitative evaluation of breast ultrasound segmentation networks with public datasets","volume":"50","author":"Thomas","year":"2023","journal-title":"Med. Phys."},{"key":"10.1016\/j.cmpb.2026.109403_bib0028","doi-asserted-by":"crossref","first-page":"1","DOI":"10.7717\/peerj-cs.1638","article-title":"Ultrasound image segmentation based on Transformer and U-Net with joint loss","volume":"9","author":"Cai","year":"2023","journal-title":"PeerJ. Comput. Sci."},{"issue":"12","key":"10.1016\/j.cmpb.2026.109403_bib0029","first-page":"1","article-title":"High-resolution ultrasound data for AI-based segmentation in mouse brain tumor","volume":"1","author":"Dorosti","year":"2025","journal-title":"Sci. Data"},{"key":"10.1016\/j.cmpb.2026.109403_bib0023","doi-asserted-by":"crossref","first-page":"891","DOI":"10.7863\/jum.2010.29.6.891","article-title":"Volume of preclinical xenograft tumors is more accurately assessed by ultrasound imaging than manual caliper measurements","volume":"29","author":"Ayers","year":"2010","journal-title":"J. Ultrasound. Med."},{"key":"10.1016\/j.cmpb.2026.109403_bib0024","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1007\/s10911-024-09555-3","article-title":"Fast ultrasound scanning is a rapid, sensitive, precise and cost-effective method to monitor tumor grafts in mice","volume":"29","author":"Moli\u00e8re","year":"2024","journal-title":"J. Mammary. Gland. Biol. Neoplasia"},{"key":"10.1016\/j.cmpb.2026.109403_bib0030","doi-asserted-by":"crossref","first-page":"1897","DOI":"10.1016\/j.ultrasmedbio.2006.06.027","article-title":"Subsample interpolation strategies for sensorless freehand 3D ultrasound","volume":"32","author":"Housden","year":"2006","journal-title":"Ultrasound. Med. Biol."},{"key":"10.1016\/j.cmpb.2026.109403_bib0025","first-page":"97","article-title":"TransUNet: rethinking the U-net architecture design for medical image segmentation through the lens of transformers","author":"Chen","year":"2024","journal-title":"Med. Image Anal."},{"key":"10.1016\/j.cmpb.2026.109403_bib0026","series-title":"Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics","first-page":"272","article-title":"Swin UNETR: Swin transformers for semantic segmentation of brain tumors in MRI images","author":"Hatamizadeh","year":"2022"},{"key":"10.1016\/j.cmpb.2026.109403_fur1","doi-asserted-by":"crossref","unstructured":"Y. Wang, T. Fu, C. Wu, J. Fan, H. Song, D. Xiao, Y. Lin, F. Liu, J. Yang, Adaptive tetrahedral interpolation for reconstruction of uneven freehand 3D ultrasound, Phys. Med. Biol. 68 (2023) 055005, doi:10.1088\/1361-6560\/ACB88C.","DOI":"10.1088\/1361-6560\/acb88c"},{"key":"10.1016\/j.cmpb.2026.109403_fur2","article-title":"Probe sector matching for Freehand 3D ultrasound reconstruction","volume":"20","author":"Chen","year":"2020","journal-title":"Sensors"}],"container-title":["Computer Methods and Programs in Biomedicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0169260726001574?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0169260726001574?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T17:12:33Z","timestamp":1783185153000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0169260726001574"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":32,"alternative-id":["S0169260726001574"],"URL":"https:\/\/doi.org\/10.1016\/j.cmpb.2026.109403","relation":{},"ISSN":["0169-2607"],"issn-type":[{"value":"0169-2607","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"From 2D to 3D: Automated ultrasound segmentation and cross-sectional validation in murine tumor models","name":"articletitle","label":"Article Title"},{"value":"Computer Methods and Programs in Biomedicine","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.cmpb.2026.109403","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"109403"}}