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Real-time corneal segmentation and 3D needle tracking in intrasurgical OCT.
Keller, Brenton; Draelos, Mark; Tang, Gao; Farsiu, Sina; Kuo, Anthony N; Hauser, Kris; Izatt, Joseph A.
Afiliação
  • Keller B; Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA.
  • Draelos M; Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA.
  • Tang G; Department of Mechanical Engineering, Duke University, Durham, NC 27708, USA.
  • Farsiu S; Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA.
  • Kuo AN; Department of Ophthalmology, Duke University Medical Center, Durham NC 27710, USA.
  • Hauser K; Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA.
  • Izatt JA; Department of Ophthalmology, Duke University Medical Center, Durham NC 27710, USA.
Biomed Opt Express ; 9(6): 2716-2732, 2018 Jun 01.
Article em En | MEDLINE | ID: mdl-30258685
Ophthalmic procedures demand precise surgical instrument control in depth, yet standard operating microscopes supply limited depth perception. Current commercial microscope-integrated optical coherence tomography partially meets this need with manually-positioned cross-sectional images that offer qualitative estimates of depth. In this work, we present methods for automatic quantitative depth measurement using real-time, two-surface corneal segmentation and needle tracking in OCT volumes. We then demonstrate these methods for guidance of ex vivo deep anterior lamellar keratoplasty (DALK) needle insertions. Surgeons using the output of these methods improved their ability to reach a target depth, and decreased their incidence of corneal perforations, both with statistical significance. We believe these methods could increase the success rate of DALK and thereby improve patient outcomes.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Qualitative_research Idioma: En Ano de publicação: 2018 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Qualitative_research Idioma: En Ano de publicação: 2018 Tipo de documento: Article