{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T13:42:14Z","timestamp":1788183734171,"version":"build-2803163510"},"reference-count":23,"publisher":"Wiley","issue":"3","license":[{"start":{"date-parts":[[2021,1,28]],"date-time":"2021-01-28T00:00:00Z","timestamp":1611792000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["aapm.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Medical Physics"],"published-print":{"date-parts":[[2021,3]]},"abstract":"<jats:sec><jats:title>Purpose<\/jats:title><jats:p>New radiation therapy protocols, in particular adaptive, focal or boost brachytherapy treatments, require determining precisely the position and orientation of the implanted radioactive seeds from real\u2010time ultrasound (US) images. This is necessary to compare them to the planned one and to adjust automatically the dosimetric plan accordingly for next seeds implantations. The image modality, the small size of the seeds, and the artifacts they produce make it a very challenging problem. The objective of the presented work is to setup and to evaluate a robust and automatic method for seed localization in three\u2010dimensional (3D) US images.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>The presented method is based on a prelocalization of the needles through which the seeds are injected in the prostate. This prelocalization allows focusing the search on a region of interest (ROI) around the needle tip. Seeds localization starts by binarizing the ROI and removing false positives using, respectively, a Bayesian classifier and a support vector machine (SVM). This is followed by a registration stage using first an iterative closest point (ICP) for localizing the connected set of seeds (named strand) inserted through a needle, and secondly refining each seed position using sum of squared differences (SSD) as a similarity criterion. ICP registers a geometric model of the strand to the candidate voxels while SSD compares an appearance model of a single seed to a subset of the image. The method was evaluated both for 3D images of an Agar\u2010agar phantom and a dataset of clinical 3D images. It was tested on stranded and on loose seeds.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Results on phantom and clinical images were compared with a manual localization giving mean errors of 1.09\u00a0\u00b1\u00a00.61\u00a0mm on phantom image and 1.44\u00a0\u00b1\u00a00.45\u00a0mm on clinical images. On clinical images, the mean errors of individual seeds orientation was<jats:inline-graphic xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" xlink:href=\"graphic\/mp14628-math-0001.png\" xlink:title=\"urn:x-wiley:00942405:media:mp14628:mp14628-math-0001\"\/>.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The proposed algorithm for radioactive seed localization is robust, tested on different US images, accurate, giving small mean error values, and returns the five cylindrical seeds degrees of freedom.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.14628","type":"journal-article","created":{"date-parts":[[2021,1,29]],"date-time":"2021-01-29T04:01:00Z","timestamp":1611892860000},"page":"1144-1156","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Machine learning and registration for automatic seed localization in 3D US images for prostate brachytherapy"],"prefix":"10.1002","volume":"48","author":[{"given":"Hatem","family":"Younes","sequence":"first","affiliation":[{"name":"University of Grenoble Alpes CNRS, TIMC\u2010IMAG F\u201038000 Grenoble France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jocelyne","family":"Troccaz","sequence":"additional","affiliation":[{"name":"University of Grenoble Alpes CNRS, TIMC\u2010IMAG F\u201038000 Grenoble France"},{"name":"Grenoble INP INSERM F\u201038000 Grenoble France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sandrine","family":"Voros","sequence":"additional","affiliation":[{"name":"University of Grenoble Alpes CNRS, TIMC\u2010IMAG F\u201038000 Grenoble France"},{"name":"Grenoble INP INSERM F\u201038000 Grenoble France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,1,28]]},"reference":[{"key":"e_1_2_7_2_2","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21492"},{"key":"e_1_2_7_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2017.06.1229"},{"key":"e_1_2_7_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2018.09.041"},{"key":"e_1_2_7_5_2","doi-asserted-by":"crossref","unstructured":"KuoN LeeJ TempanyC StuberM PrinceJ.MRI\u2010based prostate brachytherapy seed localization. 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