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Current techniques lack in accuracy and\/or line-of-sight constraints (conventional tracking systems) or expose the patient to prohibitive ionizing radiation (intra-operative CT). A possible solution is to capture the instrument with a c-arm at irregular intervals and recover the pose from the image.<\/jats:p>\n<\/jats:sec><jats:sec>\n<jats:title>Methods<\/jats:title>\n<jats:p>i3PosNet infers the position and orientation of instruments from images using a pose estimation network. Said framework considers localized patches and outputs pseudo-landmarks. The pose is reconstructed from pseudo-landmarks by geometric considerations.<\/jats:p>\n<\/jats:sec><jats:sec>\n<jats:title>Results<\/jats:title>\n<jats:p>We show i3PosNet reaches errors <jats:inline-formula><jats:alternatives><jats:tex-math>$$&lt;\\,0.05$$<\/jats:tex-math><mml:math xmlns:mml=\"http:\/\/www.w3.org\/1998\/Math\/MathML\"><mml:mrow><mml:mo>&lt;<\/mml:mo><mml:mspace\/><mml:mn>0.05<\/mml:mn><\/mml:mrow><\/mml:math><\/jats:alternatives><\/jats:inline-formula>\u00a0mm. It outperforms conventional image registration-based approaches reducing average and maximum errors by at least two thirds. i3PosNet trained on synthetic images generalizes to real X-rays without any further adaptation.<\/jats:p>\n<\/jats:sec><jats:sec>\n<jats:title>Conclusion<\/jats:title>\n<jats:p>The translation of deep learning-based methods to surgical applications is difficult, because large representative datasets for training and testing are not available. This work empirically shows sub-millimeter pose estimation trained solely based on synthetic training data.<\/jats:p>\n<\/jats:sec>","DOI":"10.1007\/s11548-020-02157-4","type":"journal-article","created":{"date-parts":[[2020,5,21]],"date-time":"2020-05-21T05:02:41Z","timestamp":1590037361000},"page":"1137-1145","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["i3PosNet: instrument pose estimation from X-ray in temporal bone surgery"],"prefix":"10.1007","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4101-819X","authenticated-orcid":false,"given":"David","family":"K\u00fcgler","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jannik","family":"Sehring","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrei","family":"Stefanov","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Igor","family":"Stenin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Julia","family":"Kristin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thomas","family":"Klenzner","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"J\u00f6rg","family":"Schipper","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0669-4018","authenticated-orcid":false,"given":"Anirban","family":"Mukhopadhyay","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,5,21]]},"reference":[{"issue":"1","key":"2157_CR1","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1097\/MAO.0000000000000914","volume":"37","author":"J Ans\u00f3","year":"2016","unstructured":"Ans\u00f3 J, D\u00fcr C, Gavaghan K, Rohrbach H, Gerber N, Williamson T, Calvo EM, Balmer TW, Precht C, Ferrario D, Dettmer MS, R\u00f6sler KM, Caversaccio MD, Bell B, Weber S (2016) A neuromonitoring approach to facial nerve preservation during image-guided robotic cochlear implantation. 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