Kügler, David ; Sehring, Jannik ; Stefanov, Andrei ; Stenin, Igor ; Kristin, Julia ; Klenzner, Thomas ; Schipper, Jörg ; Mukhopadhyay, Anirban (2020)
i3PosNet: instrument pose estimation from X-ray in temporal bone surgery.
In: International Journal of Computer Assisted Radiology and Surgery, 15 (7)
doi: 10.1007/s11548-020-02157-4
Artikel, Bibliographie
Kurzbeschreibung (Abstract)
PURPOSE:Accurate estimation of the position and orientation (pose) of surgical instruments is crucial for delicate minimally invasive temporal bone surgery. 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. METHODS: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. RESULTS:We show i3PosNet reaches errors [Formula: see text] mm. 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. CONCLUSION: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.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2020 |
| Autor(en): | Kügler, David ; Sehring, Jannik ; Stefanov, Andrei ; Stenin, Igor ; Kristin, Julia ; Klenzner, Thomas ; Schipper, Jörg ; Mukhopadhyay, Anirban |
| Art des Eintrags: | Bibliographie |
| Titel: | i3PosNet: instrument pose estimation from X-ray in temporal bone surgery |
| Sprache: | Englisch |
| Publikationsjahr: | Juli 2020 |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | International Journal of Computer Assisted Radiology and Surgery |
| Jahrgang/Volume einer Zeitschrift: | 15 |
| (Heft-)Nummer: | 7 |
| DOI: | 10.1007/s11548-020-02157-4 |
| URL / URN: | https://doi.org/10.1007/s11548-020-02157-4 |
| Kurzbeschreibung (Abstract): | PURPOSE:Accurate estimation of the position and orientation (pose) of surgical instruments is crucial for delicate minimally invasive temporal bone surgery. 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. METHODS: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. RESULTS:We show i3PosNet reaches errors [Formula: see text] mm. 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. CONCLUSION: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. |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme |
| Hinterlegungsdatum: | 26 Jun 2020 07:52 |
| Letzte Änderung: | 26 Jun 2020 07:52 |
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