June 2017
Volume 58, Issue 8
Open Access
ARVO Annual Meeting Abstract  |   June 2017
Retinal Surface Detection in intraoperative Optical Coherence Tomography (iOCT)
Author Affiliations & Notes
  • Moritz Pettenkofer
    Ophthalmology, Munich Tech University, Munich, Germany
  • M. Ali Nasseri
    Ophthalmology, Munich Tech University, Munich, Germany
  • Mingchuan Zhou
    Ophthalmology, Munich Tech University, Munich, Germany
  • Chris Lohmann
    Ophthalmology, Munich Tech University, Munich, Germany
  • Footnotes
    Commercial Relationships   Moritz Pettenkofer, None; M. Ali Nasseri, None; Mingchuan Zhou, None; Chris Lohmann, None
  • Footnotes
    Support  none
Investigative Ophthalmology & Visual Science June 2017, Vol.58, 680. doi:
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    • Get Citation

      Moritz Pettenkofer, M. Ali Nasseri, Mingchuan Zhou, Chris Lohmann; Retinal Surface Detection in intraoperative Optical Coherence Tomography (iOCT). Invest. Ophthalmol. Vis. Sci. 2017;58(8):680.

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      © ARVO (1962-2015); The Authors (2016-present)

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Purpose : Goal of the present study is to investigate retinal surface detection and precision using intraoperative OCT imaging. The precise tracking of the individual retinal layers may help to achieve a higher reliability and safety in macular micro-surgery due to a higher control level of intraoperative actions such as sub-retinal injections, epiretinal membrane peel and macular hole surgery.

Methods : 20 eyes of 20 patients with various retinal diseases such as full-thickness macular hole, macular pucker, diabetic retinopathy or vitreous hemorrhage were randomly included in this study. All images were obtained from Zeiss Lumera 700 Rescan Microscope. Surface pixels were detected by image processing methods, using a tracking algorithm. For each eye 20 detections were performed to evaluate the repeatability of the method.

Results : The mean detected surface precision was shown as 12.1104 pixels. The minimum tracking error was investigated to be 6.00 pixels and the maximum 32.00 pixels.

Conclusions : In the present study we introduced a tracking algorithm for retinal surface detection using intraoperative OCT images. Our method could be applicable for current clinical routines as well as future vitreoretinal diagnosis and treatments. It is the first step towards future technologies in retinal tracking unsable for sub-retinal injection, intraoperative retinal vein detection and tracking such as intraoperative macular hole analysis.

This is an abstract that was submitted for the 2017 ARVO Annual Meeting, held in Baltimore, MD, May 7-11, 2017.



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