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Two image processing pipelines based on U-Net and YOLO architectures were developed and evaluated on a data set of T1- and T2-weighted MRI with voxel sizes ranging from 0.6 to 1.6\u00a0mm. Detection performance is evaluated using the F1-score, whereas localization is evaluated using two metrics that describe the deviation of the predicted position from the true position. Although the benchmark method, a conventional image processing pipeline based on connected component analysis achieved marginally lower positioning errors, the neural network approaches outperformed it in terms of detection performance, especially by reducing false negatives. The results show that both pipelines achieve accurate marker detection and localization, with U-Net slightly outperforming YOLO in terms of positioning accuracy. A key advantage of the neural network-based pipelines is their ability to handle markers with non-uniform or incomplete appearance, which enhances their robustness in real-world scenarios and provides flexibility by eliminating the need for manual parameter adjustments. While neural networks offer the advantage that they can be easily adapted to various imaging conditions, their dependence on training data can be a limitation. The results suggest that neural network based pipelines offer a robust alternative for fiducial marker detection and localization.<\/jats:p>","DOI":"10.1007\/s11760-025-04118-3","type":"journal-article","created":{"date-parts":[[2025,5,12]],"date-time":"2025-05-12T06:07:42Z","timestamp":1747030062000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Neural network-based localization of spherical MRI markers"],"prefix":"10.1007","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6621-3944","authenticated-orcid":false,"given":"Christian","family":"Fiedler","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0980-6487","authenticated-orcid":false,"given":"Silke","family":"Kolbig","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,5,12]]},"reference":[{"issue":"2","key":"4118_CR1","doi-asserted-by":"publisher","first-page":"694","DOI":"10.1002\/mp.16035","volume":"50","author":"KM Kirby","year":"2023","unstructured":"Kirby, K.M., Koons, E.K., Welker, K.M., Fagan, A.J.: Minimizing magnetic resonance image geometric distortion at 7 tesla for frameless presurgical planning using skin-adhered fiducials. 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