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We also propose a multi-task learning strategy in which our model is trained to perform both accurate electrode detection and catheter segmentation simultaneously. A key challenge with this approach is achieving optimal performance for both tasks. To address this, we introduce a novel multi-level dynamic resource prioritization method. This method dynamically adjusts sample and task weights during training to effectively prioritize more challenging tasks, where task difficulty is inversely proportional to performance and evolves throughout the training process.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The proposed method has been validated on both public and private datasets for single-task catheter segmentation and multi-task catheter segmentation and detection. The performance of our method is also compared with existing state-of-the-art methods, demonstrating significant improvements, with a mean\n                      <jats:inline-formula>\n                        <jats:alternatives>\n                          <jats:tex-math>$$\\mathcal {J}$$<\/jats:tex-math>\n                          <mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\">\n                            <mml:mi>J<\/mml:mi>\n                          <\/mml:math>\n                        <\/jats:alternatives>\n                      <\/jats:inline-formula>\n                      of 64.37\/63.97 and with average precision over all IoU thresholds of 84.15\/83.13, respectively, for detection and segmentation multi-task on the validation and test sets of the catheter detection and segmentation dataset.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Our approach achieves a good balance between accuracy and efficiency, making it well-suited for real-time surgical guidance applications.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s11548-025-03461-7","type":"journal-article","created":{"date-parts":[[2025,6,27]],"date-time":"2025-06-27T06:18:44Z","timestamp":1751005124000},"page":"163-173","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Catheter detection and segmentation in X-ray images via multi-task learning"],"prefix":"10.1007","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6075-5614","authenticated-orcid":false,"given":"Lin","family":"Xi","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5770-5843","authenticated-orcid":false,"given":"Yingliang","family":"Ma","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ethan","family":"Koland","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sandra","family":"Howell","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aldo","family":"Rinaldi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kawal S.","family":"Rhode","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,6,27]]},"reference":[{"issue":"8","key":"3461_CR1","doi-asserted-by":"publisher","first-page":"931","DOI":"10.1093\/europace\/eun145","volume":"10","author":"S Knecht","year":"2008","unstructured":"Knecht S, Skali H, O\u2019Neill MD, Wright M, Matsuo S, Chaudhry GM, Haffajee CI, Nault I, Gijsbers GHM, Sacher F, Laurent F, Montaudon M, Corneloup O, Hocini M, Ha\u00efssaguerre M, Orlov MV, Ja\u00efs P (2008) Computed tomography\u2013fluoroscopy overlay evaluation during catheter ablation of left atrial arrhythmia. 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