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However, accuracy can be affected by source-detector trajectory deviations caused by gravitational artifacts and mechanical instabilities. This study addresses calibration challenges and suggests leveraging interventional devices with radio-opaque markers to optimize C-arm geometry.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Methods<\/jats:title>\n            <jats:p>We propose an online calibration method using image-specific features derived from interventional devices like guidewires and catheters (In the remainder of this paper, the term\u201dcatheter\u201d will refer to both catheter and guidewire). The process begins with gantry-recorded data, refined through iterative nonlinear optimization. A machine learning approach detects and segments elongated devices by identifying candidates via thresholding on a weighted sum of curvature, derivative, and high-frequency indicators. An ensemble classifier segments these regions, followed by post-processing to remove false positives, integrating vessel maps, manual correction and identification markers. An interpolation step filling gaps along the catheter.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>Among the optimized ensemble classifiers, the one trained on the first frames achieved the best performance, with a specificity of 99<jats:italic>.<\/jats:italic>43% and precision of 86<jats:italic>.<\/jats:italic>41%. The calibration method was evaluated on three clinical datasets and four phantom angiogram pairs, reducing the mean backprojection error from 4<jats:italic>.<\/jats:italic>11\u2009\u00b1\u20092<jats:italic>.<\/jats:italic>61 to 0<jats:italic>.<\/jats:italic>15\u2009\u00b1\u20090<jats:italic>.<\/jats:italic>01\u00a0mm. Additionally, 3D accuracy analysis showed an average root mean square error of 3<jats:italic>.<\/jats:italic>47% relative to the true marker distance.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusions<\/jats:title>\n            <jats:p>This study explores using interventional tools with radio-opaque markers for C-arm self-calibration. The proposed method significantly reduces 2D backprojection error and 3D RMSE, enabling accurate 3D vascular reconstruction.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1007\/s11548-025-03434-w","type":"journal-article","created":{"date-parts":[[2025,6,26]],"date-time":"2025-06-26T05:50:49Z","timestamp":1750917049000},"page":"1875-1888","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Semi-automatic segmentation of elongated interventional instruments for online calibration of C-arm imaging system"],"prefix":"10.1007","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9530-8133","authenticated-orcid":false,"given":"Negar","family":"Chabi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alfredo","family":"Illanes","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Oliver","family":"Beuing","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Daniel","family":"Behme","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bernhard","family":"Preim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sylvia","family":"Saalfeld","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,6,26]]},"reference":[{"key":"3434_CR1","doi-asserted-by":"crossref","unstructured":"Zweng M, Fallavollita P, Demirci S, Kowarschik M, Navab N, Mateus D (2015) Automatic guide-wire detection for neurointerventions using low-rank sparse matrix decomposition and denoising. 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