Abstract

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.

© 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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2018 (1)

N. Gessert, M. Schlüter, and A. Schlaefer, “A deep learning approach for pose estimation from volumetric OCT data,” Medical Image Analysis 46, 162–179 (2018).
[Crossref] [PubMed]

2017 (6)

S. Shin, J. K. Bae, Y. Ahn, H. Kim, G. Choi, Y.-S. Yoo, C.-K. Joo, S. Moon, and W. Jung, “Lamellar keratoplasty using position-guided surgical needle and M-mode optical coherence tomography,” J. Biomed. Opt. 22, 125005 (2017).
[Crossref]

O. Carrasco-Zevallos, C. Viehland, B. Keller, A. N. Kuo, C. A. Toth, and J. A. Izatt, “Microscope-integrated OCT at 800 kHz line rate for high speed 4D imaging of ophthalmic surgery,” Invest. Ophthalmol. Vis. Sci. 58, 3813 (2017).

M. T. El-Haddad, I. Bozic, and Y. K. Tao, “Spectrally encoded coherence tomography and reflectometry (SECTR): simultaneous en face and cross-sectional imaging at 2 gigapixels-per-second,” J. Biophoton. 11, e201700268 (2017).
[Crossref]

T. Klein and R. Huber, “High-speed OCT light sources and systems,” Biomed. Opt. Express 8, 828–859 (2017).
[Crossref] [PubMed]

O. M. Carrasco-Zevallos, C. Viehland, B. Keller, M. Draelos, A. N. Kuo, C. A. Toth, and J. A. Izatt, “Review of intraoperative optical coherence tomography: technology and applications,” Biomed. Opt. Express 8, 1607–1637 (2017).
[Crossref] [PubMed]

L. Fang, D. Cunefare, C. Wang, R. H. Guymer, S. Li, and S. Farsiu, “Automatic segmentation of nine retinal layer boundaries in oct images of non-exudative amd patients using deep learning and graph search,” Biomed. Opt. Express 8, 2732–2744 (2017).
[Crossref] [PubMed]

2016 (6)

L. Shen, O. Carrasco-Zevallos, B. Keller, C. Viehland, G. Waterman, P. S. Hahn, A. N. Kuo, C. A. Toth, and J. A. Izatt, “Novel microscope-integrated stereoscopic heads-up display for intrasurgical optical coherence tomography,” Biomed. Opt. Express 7, 1711–1726 (2016).
[Crossref]

B. Keller, D. Cunefare, D. S. Grewal, T. H. Mahmoud, J. A. Izatt, and S. Farsiu, “Length-adaptive graph search for automatic segmentation of pathological features in optical coherence tomography images,” J. Biomed. Opt. 21, 076015 (2016).
[Crossref]

D. Williams, Y. Zheng, P. G. Davey, F. Bao, M. Shen, and A. Elsheikh, “Reconstruction of 3D surface maps from anterior segment optical coherence tomography images using graph theory and genetic algorithms,” Biomed. Signal Process. Control 25, 91–98 (2016).
[Crossref]

N. D. Pasricha, C. Shieh, O. M. Carrasco-Zevallos, B. Keller, D. Cunefare, J. S. Mehta, S. Farsiu, J. A. Izatt, C. A. Toth, and A. N. Kuo, “Needle depth and big-bubble success in deep anterior lamellar keratoplasty: An ex vivo microscope-integrated OCT study,” Cornea 35, 1471–1477 (2016).
[Crossref] [PubMed]

O. Carrasco-Zevallos, B. Keller, C. Viehland, L. Shen, G. Waterman, B. Todorich, C. Shieh, P. Hahn, S. Farsiu, A. Kuo, C. A. Toth, and J. A. Izatt, “Live volumetric (4D) visualization and guidance of in vivo human ophthalmic surgery with intraoperative optical coherence tomography,” Sci. Rep. 6, 31689 (2016).
[Crossref] [PubMed]

S. Siebelmann, P. Steven, D. Hos, G. Hüttmann, E. Lankenau, B. Bachmann, and C. Cursiefen, “Advantages of microscope-integrated intraoperative online optical coherence tomography: usage in boston keratoprosthesis type I surgery,” J. Biomed. Opt. 21, 016005 (2016).
[Crossref]

2015 (10)

C. I. Falkner-Radler, C. Glittenberg, M. Gabriel, and S. Binder, “Intrasurgical microscope-integrated spectral domain optical coherence tomography–assisted membrane peeling,” Retina 35, 2100–2106 (2015).
[Crossref] [PubMed]

M. Pfau, S. Michels, S. Binder, and M. D. Becker, “Clinical experience with the first commercially available intraoperative optical coherence tomography system,” Ophthalmic Surgery, Lasers and Imaging Retina 46, 1001–1008 (2015).
[Crossref] [PubMed]

A. Saad, E. Guilbert, A. Grise-Dulac, P. Sabatier, and D. Gatinel, “Intraoperative OCT-assisted DMEK: 14 consecutive cases,” Cornea 34, 802–807 (2015).
[Crossref] [PubMed]

X. Li, L. Wei, X. Dong, P. Huang, C. Zhang, Y. He, G. Shi, and Y. Zhang, “Microscope-integrated optical coherence tomography for image-aided positioning of glaucoma surgery,” J. Biomed. Opt. 20, 076001 (2015).
[Crossref]

J. P. Ehlers, J. Goshe, W. J. Dupps, P. K. Kaiser, R. P. Singh, R. Gans, J. Eisengart, and S. K. Srivastava, “Determination of feasibility and utility of microscope-integrated optical coherence tomography during ophthalmic surgery: the DISCOVER study RESCAN results,” JAMA Ophthalmology 133, 1124–1132 (2015).
[Crossref] [PubMed]

J. Tian, B. Varga, G. M. Somfai, W.-H. Lee, W. E. Smiddy, and D. C. DeBuc, “Real-time automatic segmentation of optical coherence tomography volume data of the macular region,” PloS One 10, e0133908 (2015).
[Crossref] [PubMed]

J. Xu, K. S. Wong, V. Wong, M. Heisler, S. Lee, M. Cua, Y. Jian, and M. V. Sarunic, “Enhancing the visualization of human retina vascular networks by graphics processing unit accelerated speckle variance OCT and graph cut retinal layer segmentation,” Proc. SPIE 9132, 93122H (2015).

P. Hahn, O. Carrasco-Zevallos, D. Cunefare, J. Migacz, S. Farsiu, J. A. Izatt, and C. A. Toth, “Intrasurgical human retinal imaging with manual instrument tracking using a microscope-integrated spectral-domain optical coherence tomography device,” Translational Vision Science & Technology 4, 1 (2015).
[Crossref]

D. Kaba, Y. Wang, C. Wang, X. Liu, H. Zhu, A. Salazar-Gonzalez, and Y. Li, “Retina layer segmentation using kernel graph cuts and continuous max-flow,” Opt. Express 23, 7366–7384 (2015).
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M. T. El-Haddad and Y. K. Tao, “Automated stereo vision instrument tracking for intraoperative OCT guided anterior segment ophthalmic surgical maneuvers,” Biomed. Opt. Express 6, 3014–3031 (2015).
[Crossref] [PubMed]

2014 (4)

Y. K. Tao, S. K. Srivastava, and J. P. Ehlers, “Microscope-integrated intraoperative OCT with electrically tunable focus and heads-up display for imaging of ophthalmic surgical maneuvers,” Biomed. Opt. Express 5, 1877–1885 (2014).
[Crossref] [PubMed]

W. Wieser, W. Draxinger, T. Klein, S. Karpf, T. Pfeiffer, and R. Huber, “High definition live 3D-OCT in vivo: design and evaluation of a 4D OCT engine with 1 GVoxel/s,” Biomed. Opt. Express 5, 2963–2977 (2014).
[Crossref] [PubMed]

P. Steven, C. Le Blanc, E. Lankenau, M. Krug, S. Oelckers, L. M. Heindl, U. Gehlsen, G. Huettmann, and C. Cursiefen, “Optimising deep anterior lamellar keratoplasty (DALK) using intraoperative online optical coherence tomography (iOCT),” Br. J. Ophthalmol. 98, 900–904 (2014).
[Crossref] [PubMed]

J. P. Ehlers, S. K. Srivastava, D. Feiler, A. I. Noonan, A. M. Rollins, and Y. K. Tao, “Integrative advances for OCT-guided ophthalmic surgery and intraoperative OCT: microscope integration, surgical instrumentation, and heads-up display surgeon feedback,” PloS one 9, e105224 (2014).
[Crossref] [PubMed]

2013 (6)

P. Steven, C. Le Blanc, K. Velten, E. Lankenau, M. Krug, S. Oelckers, L. M. Heindl, U. Gehlsen, G. Hüttmann, and C. Cursiefen, “Optimizing descemet membrane endothelial keratoplasty using intraoperative optical coherence tomography,” JAMA Ophthalmology 131, 1135–1142 (2013).
[Crossref] [PubMed]

P. A. Dufour, L. Ceklic, H. Abdillahi, S. Schroder, S. De Dzanet, U. Wolf-Schnurrbusch, and J. Kowal, “Graph-based multi-surface segmentation of oct data using trained hard and soft constraints,” IEEE Trans. Med. Imag. 32, 531–543 (2013).
[Crossref]

D. Williams, Y. Zheng, F. Bao, and A. Elsheikh, “Automatic segmentation of anterior segment optical coherence tomography images,” J. Biomed. Opt. 18, 056003 (2013).
[Crossref]

Y. Jian, K. Wong, and M. V. Sarunic, “Graphics processing unit accelerated optical coherence tomography processing at megahertz axial scan rate and high resolution video rate volumetric rendering,” J. Biomed. Opt. 18, 026002 (2013).
[Crossref]

J. P. Ehlers, Y. K. Tao, S. Farsiu, R. Maldonado, J. A. Izatt, and C. A. Toth, “Visualization of real-time intraoperative maneuvers with a microscope-mounted spectral domain optical coherence tomography system,” Retina 33, 232 (2013).
[Crossref]

B. J. Antony, M. D. Abràmoff, M. M. Harper, W. Jeong, E. H. Sohn, Y. H. Kwon, R. Kardon, and M. K. Garvin, “A combined machine-learning and graph-based framework for the segmentation of retinal surfaces in SD-OCT volumes,” Biomed. Opt. Express 4, 2712–2728 (2013).
[Crossref]

2012 (8)

K. K. Lee, A. Mariampillai, X. Joe, D. W. Cadotte, B. C. Wilson, B. A. Standish, and V. X. Yang, “Real-time speckle variance swept-source optical coherence tomography using a graphics processing unit,” Biomed. Opt. Express 3, 1557–1564 (2012).
[Crossref] [PubMed]

D. hak Choi, H. Hiro-Oka, K. Shimizu, and K. Ohbayashi, “Spectral domain optical coherence tomography of multi-MHz A-scan rates at 1310 nm range and real-time 4D-display up to 41 volumes/second,” Biomed. Opt. Express 3, 3067–3086 (2012).
[Crossref]

J. U. Kang, Y. Huang, J. Cha, K. Zhang, Z. Ibrahim, W. A. Lee, G. Brandacher, and P. Gehlbach, “Real-time three-dimensional fourier-domain optical coherence tomography video image guided microsurgeries,” J. Biomed. Opt. 17, 081403 (2012).
[Crossref] [PubMed]

V. M. Borderie, O. Sandali, J. Bullet, T. Gaujoux, O. Touzeau, and L. Laroche, “Long-term results of deep anterior lamellar versus penetrating keratoplasty,” Ophthalmology 119, 249–255 (2012).
[Crossref]

S. J. Chiu, J. A. Izatt, R. V. O’Connell, K. P. Winter, C. A. Toth, and S. Farsiu, “Validated automatic segmentation of AMD pathology including drusen and geographic atrophy in SD-OCT images,” Invest. Ophthalmol. Vis. Sci. 53, 53–61 (2012).
[Crossref]

P. Shu and Y. Sun, “Automated extraction of the inner contour of the anterior chamber using optical coherence tomography images,” Journal of Innovative Optical Health Sciences 05, 1250030 (2012).
[Crossref]

D. Smadja, J. Colin, R. R. Krueger, G. R. Mello, A. Gallois, B. Mortemousque, and D. Touboul, “Outcomes of deep anterior lamellar keratoplasty for keratoconus: learning curve and advantages of the big bubble technique,” Cornea 31, 859–863 (2012).
[Crossref] [PubMed]

U. K. Bhatt, U. Fares, I. Rahman, D. G. Said, S. V. Maharajan, and H. S. Dua, “Outcomes of deep anterior lamellar keratoplasty following successful and failed ‘big bubble’,” Br. J. Ophthalmol. 96, 564–569 (2012).
[Crossref]

2011 (4)

S. Binder, C. I. Falkner-Radler, C. Hauger, H. Matz, and C. Glittenberg, “Feasibility of intrasurgical spectral-domain optical coherence tomography,” Retina 31, 1332–1336 (2011).
[Crossref] [PubMed]

J. P. Ehlers, Y. K. Tao, S. Farsiu, R. Maldonado, J. A. Izatt, and C. A. Toth, “Integration of a spectral domain optical coherence tomography system into a surgical microscope for intraoperative imaging,” Invest. Ophthalmol. Vis. Sci. 52, 3153–3159 (2011).
[Crossref] [PubMed]

K. Zhang and J. U. Kang, “Real-time intraoperative 4D full-range FD-OCT based on the dual graphics processing units architecture for microsurgery guidance,” Biomed. Opt. Express 2, 764–770 (2011).
[Crossref] [PubMed]

F. LaRocca, S. J. Chiu, R. P. McNabb, A. N. Kuo, J. A. Izatt, and S. Farsiu, “Robust automatic segmentation of corneal layer boundaries in SDOCT images using graph theory and dynamic programming,” Biomed. Opt. Express 2, 1524–1538 (2011).
[Crossref] [PubMed]

2010 (5)

2009 (4)

S. H. Chavala, S. Farsiu, R. Maldonado, D. K. Wallace, S. F. Freedman, and C. A. Toth, “Insights into advanced retinopathy of prematurity using handheld spectral domain optical coherence tomography imaging,” Ophthalmology 116, 2448–2456 (2009).
[Crossref] [PubMed]

P. N. Dayani, R. Maldonado, S. Farsiu, and C. A. Toth, “Intraoperative use of handheld spectral domain optical coherence tomography imaging in macular surgery,” Retina 29, 1457 (2009).
[Crossref] [PubMed]

D. C. Han, J. S. Mehta, Y. M. Por, H. M. Htoon, and D. T. Tan, “Comparison of outcomes of lamellar keratoplasty and penetrating keratoplasty in keratoconus,” Am. J. Ophthalmol. 148, 744–751 (2009).
[Crossref] [PubMed]

D. Hedeker, H. Demirtas, and R. J. Mermelstein, “A mixed ordinal location scale model for analysis of ecological momentary assessment (EMA) data,” Statistics and its Interface 2, 391 (2009).
[Crossref] [PubMed]

2008 (1)

2005 (1)

G. Geerling, M. Müller, C. Winter, H. Hoerauf, S. Oelckers, H. Laqua, and R. Birngruber, “Intraoperative 2-dimensional optical coherence tomography as a new tool for anterior segment surgery,” Archives of Ophthalmology 123, 253–257 (2005).
[Crossref] [PubMed]

2004 (1)

A. Barbier and E. Galin, “Fast distance computation between a point and cylinders, cones, line-swept spheres and cone-spheres,” Journal of Graphics Tools 9, 11–19 (2004).
[Crossref]

2002 (2)

M. Anwar and K. D. Teichmann, “Big-bubble technique to bare Descemet’s membrane in anterior lamellar keratoplasty,” Journal of Cataract & Refractive Surgery 28, 398–403 (2002).
[Crossref]

Y. Li, R. Shekhar, and D. Huang, “Segmentation of 830- and 1310-nm LASIK corneal optical coherence tomography images,” Proc. SPIE 4684, 167–179 (2002).
[Crossref]

1997 (1)

R. C. Wolfs, C. C. Klaver, J. R. Vingerling, D. E. Grobbee, A. Hofman, and et al., “Distribution of central corneal thickness and its association with intraocular pressure: The rotterdam study,” Am. J. Ophthalmol. 123, 767–772 (1997).
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1994 (1)

R. B. Mandell, “Corneal power correction factor for photorefractive keratectomy,” Journal of Refractive Surgery 10, 125–128 (1994).

1992 (1)

P. J. Besl, N. D. McKay, and et al., “A method for registration of 3-D shapes,” IEEE Trans. Pattern Anal. Mach. Intell. 14, 239–256 (1992).
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1991 (2)

W. Dumouchel and F. O’Brien, “Integrating a robust option into a multiple regression computing environment,” Institute for Mathematics and Its Applications 36, 41 (1991).

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography,” Science 254, 1178 (1991).
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1977 (1)

P. W. Holland and R. E. Welsch, “Robust regression using iteratively reweighted least-squares,” Communications in Statistics-theory and Methods 6, 813–827 (1977).
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1966 (1)

A. Rosenfeld and J. L. Pfaltz, “Sequential operations in digital picture processing,” Journal of the ACM (JACM) 13, 471–494 (1966).
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1959 (1)

E. W. Dijkstra, “A note on two problems in connexion with graphs,” Numerische mathematik 1, 269–271 (1959).
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Abdillahi, H.

P. A. Dufour, L. Ceklic, H. Abdillahi, S. Schroder, S. De Dzanet, U. Wolf-Schnurrbusch, and J. Kowal, “Graph-based multi-surface segmentation of oct data using trained hard and soft constraints,” IEEE Trans. Med. Imag. 32, 531–543 (2013).
[Crossref]

Abràmoff, M. D.

Ahn, Y.

S. Shin, J. K. Bae, Y. Ahn, H. Kim, G. Choi, Y.-S. Yoo, C.-K. Joo, S. Moon, and W. Jung, “Lamellar keratoplasty using position-guided surgical needle and M-mode optical coherence tomography,” J. Biomed. Opt. 22, 125005 (2017).
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Antony, B. J.

Anwar, M.

M. Anwar and K. D. Teichmann, “Big-bubble technique to bare Descemet’s membrane in anterior lamellar keratoplasty,” Journal of Cataract & Refractive Surgery 28, 398–403 (2002).
[Crossref]

Bachmann, B.

S. Siebelmann, P. Steven, D. Hos, G. Hüttmann, E. Lankenau, B. Bachmann, and C. Cursiefen, “Advantages of microscope-integrated intraoperative online optical coherence tomography: usage in boston keratoprosthesis type I surgery,” J. Biomed. Opt. 21, 016005 (2016).
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Bae, J. K.

S. Shin, J. K. Bae, Y. Ahn, H. Kim, G. Choi, Y.-S. Yoo, C.-K. Joo, S. Moon, and W. Jung, “Lamellar keratoplasty using position-guided surgical needle and M-mode optical coherence tomography,” J. Biomed. Opt. 22, 125005 (2017).
[Crossref]

Bao, F.

D. Williams, Y. Zheng, P. G. Davey, F. Bao, M. Shen, and A. Elsheikh, “Reconstruction of 3D surface maps from anterior segment optical coherence tomography images using graph theory and genetic algorithms,” Biomed. Signal Process. Control 25, 91–98 (2016).
[Crossref]

D. Williams, Y. Zheng, F. Bao, and A. Elsheikh, “Automatic segmentation of anterior segment optical coherence tomography images,” J. Biomed. Opt. 18, 056003 (2013).
[Crossref]

Barbier, A.

A. Barbier and E. Galin, “Fast distance computation between a point and cylinders, cones, line-swept spheres and cone-spheres,” Journal of Graphics Tools 9, 11–19 (2004).
[Crossref]

Becker, M. D.

M. Pfau, S. Michels, S. Binder, and M. D. Becker, “Clinical experience with the first commercially available intraoperative optical coherence tomography system,” Ophthalmic Surgery, Lasers and Imaging Retina 46, 1001–1008 (2015).
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Bertalmio, M.

M. Bertalmio, A. L. Bertozzi, and G. Sapiro, “Navier-stokes, fluid dynamics, and image and video inpainting,” in Proceedings of the 2001 IEEE Computer Society Conference on, vol. 1 (IEEE, 2001), p. I.

Bertozzi, A. L.

M. Bertalmio, A. L. Bertozzi, and G. Sapiro, “Navier-stokes, fluid dynamics, and image and video inpainting,” in Proceedings of the 2001 IEEE Computer Society Conference on, vol. 1 (IEEE, 2001), p. I.

Besl, P. J.

P. J. Besl, N. D. McKay, and et al., “A method for registration of 3-D shapes,” IEEE Trans. Pattern Anal. Mach. Intell. 14, 239–256 (1992).
[Crossref]

Bhatt, U. K.

U. K. Bhatt, U. Fares, I. Rahman, D. G. Said, S. V. Maharajan, and H. S. Dua, “Outcomes of deep anterior lamellar keratoplasty following successful and failed ‘big bubble’,” Br. J. Ophthalmol. 96, 564–569 (2012).
[Crossref]

Binder, S.

C. I. Falkner-Radler, C. Glittenberg, M. Gabriel, and S. Binder, “Intrasurgical microscope-integrated spectral domain optical coherence tomography–assisted membrane peeling,” Retina 35, 2100–2106 (2015).
[Crossref] [PubMed]

M. Pfau, S. Michels, S. Binder, and M. D. Becker, “Clinical experience with the first commercially available intraoperative optical coherence tomography system,” Ophthalmic Surgery, Lasers and Imaging Retina 46, 1001–1008 (2015).
[Crossref] [PubMed]

S. Binder, C. I. Falkner-Radler, C. Hauger, H. Matz, and C. Glittenberg, “Feasibility of intrasurgical spectral-domain optical coherence tomography,” Retina 31, 1332–1336 (2011).
[Crossref] [PubMed]

Birngruber, R.

G. Geerling, M. Müller, C. Winter, H. Hoerauf, S. Oelckers, H. Laqua, and R. Birngruber, “Intraoperative 2-dimensional optical coherence tomography as a new tool for anterior segment surgery,” Archives of Ophthalmology 123, 253–257 (2005).
[Crossref] [PubMed]

Bizheva, K.

N. Hutchings, T. L. Simpson, C. Hyun, A. A. Moayed, S. Hariri, L. Sorbara, and K. Bizheva, “Swelling of the human cornea revealed by high-speed, ultrahigh-resolution optical coherence tomography,” Invest. Ophthalmol. Vis. Sci. 51, 4579 (2010).
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J. Eichel, A. Mishra, P. Fieguth, D. Clausi, and K. Bizheva, “A novel algorithm for extraction of the layers of the cornea,” in Canadian Conference of Computer and Robot Vision, (IEEE, 2009), pp. 313–320.

Borderie, V. M.

V. M. Borderie, O. Sandali, J. Bullet, T. Gaujoux, O. Touzeau, and L. Laroche, “Long-term results of deep anterior lamellar versus penetrating keratoplasty,” Ophthalmology 119, 249–255 (2012).
[Crossref]

Bozic, I.

M. T. El-Haddad, I. Bozic, and Y. K. Tao, “Spectrally encoded coherence tomography and reflectometry (SECTR): simultaneous en face and cross-sectional imaging at 2 gigapixels-per-second,” J. Biophoton. 11, e201700268 (2017).
[Crossref]

Brandacher, G.

J. U. Kang, Y. Huang, J. Cha, K. Zhang, Z. Ibrahim, W. A. Lee, G. Brandacher, and P. Gehlbach, “Real-time three-dimensional fourier-domain optical coherence tomography video image guided microsurgeries,” J. Biomed. Opt. 17, 081403 (2012).
[Crossref] [PubMed]

Bullet, J.

V. M. Borderie, O. Sandali, J. Bullet, T. Gaujoux, O. Touzeau, and L. Laroche, “Long-term results of deep anterior lamellar versus penetrating keratoplasty,” Ophthalmology 119, 249–255 (2012).
[Crossref]

Cadotte, D. W.

Carrasco-Zevallos, O.

O. Carrasco-Zevallos, C. Viehland, B. Keller, A. N. Kuo, C. A. Toth, and J. A. Izatt, “Microscope-integrated OCT at 800 kHz line rate for high speed 4D imaging of ophthalmic surgery,” Invest. Ophthalmol. Vis. Sci. 58, 3813 (2017).

O. Carrasco-Zevallos, B. Keller, C. Viehland, L. Shen, G. Waterman, B. Todorich, C. Shieh, P. Hahn, S. Farsiu, A. Kuo, C. A. Toth, and J. A. Izatt, “Live volumetric (4D) visualization and guidance of in vivo human ophthalmic surgery with intraoperative optical coherence tomography,” Sci. Rep. 6, 31689 (2016).
[Crossref] [PubMed]

L. Shen, O. Carrasco-Zevallos, B. Keller, C. Viehland, G. Waterman, P. S. Hahn, A. N. Kuo, C. A. Toth, and J. A. Izatt, “Novel microscope-integrated stereoscopic heads-up display for intrasurgical optical coherence tomography,” Biomed. Opt. Express 7, 1711–1726 (2016).
[Crossref]

P. Hahn, O. Carrasco-Zevallos, D. Cunefare, J. Migacz, S. Farsiu, J. A. Izatt, and C. A. Toth, “Intrasurgical human retinal imaging with manual instrument tracking using a microscope-integrated spectral-domain optical coherence tomography device,” Translational Vision Science & Technology 4, 1 (2015).
[Crossref]

C. Viehland, B. Keller, O. Carrasco-Zevallos, D. Cunefare, L. Shen, C. Toth, S. Farsiu, and J. A. Izatt, “Novel real-time volumetric tool segmentation algorithm for intraoperative microscope integrated OCT,” in SPIE BiOS (International Society for Optics and Photonics, 2016), p. 969702.

Carrasco-Zevallos, O. M.

O. M. Carrasco-Zevallos, C. Viehland, B. Keller, M. Draelos, A. N. Kuo, C. A. Toth, and J. A. Izatt, “Review of intraoperative optical coherence tomography: technology and applications,” Biomed. Opt. Express 8, 1607–1637 (2017).
[Crossref] [PubMed]

N. D. Pasricha, C. Shieh, O. M. Carrasco-Zevallos, B. Keller, D. Cunefare, J. S. Mehta, S. Farsiu, J. A. Izatt, C. A. Toth, and A. N. Kuo, “Needle depth and big-bubble success in deep anterior lamellar keratoplasty: An ex vivo microscope-integrated OCT study,” Cornea 35, 1471–1477 (2016).
[Crossref] [PubMed]

Ceklic, L.

P. A. Dufour, L. Ceklic, H. Abdillahi, S. Schroder, S. De Dzanet, U. Wolf-Schnurrbusch, and J. Kowal, “Graph-based multi-surface segmentation of oct data using trained hard and soft constraints,” IEEE Trans. Med. Imag. 32, 531–543 (2013).
[Crossref]

Cha, J.

J. U. Kang, Y. Huang, J. Cha, K. Zhang, Z. Ibrahim, W. A. Lee, G. Brandacher, and P. Gehlbach, “Real-time three-dimensional fourier-domain optical coherence tomography video image guided microsurgeries,” J. Biomed. Opt. 17, 081403 (2012).
[Crossref] [PubMed]

Chang, W.

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and J. G. Fujimoto, “Optical coherence tomography,” Science 254, 1178 (1991).
[Crossref] [PubMed]

Chavala, S. H.

S. H. Chavala, S. Farsiu, R. Maldonado, D. K. Wallace, S. F. Freedman, and C. A. Toth, “Insights into advanced retinopathy of prematurity using handheld spectral domain optical coherence tomography imaging,” Ophthalmology 116, 2448–2456 (2009).
[Crossref] [PubMed]

Chiu, S. J.

Choi, G.

S. Shin, J. K. Bae, Y. Ahn, H. Kim, G. Choi, Y.-S. Yoo, C.-K. Joo, S. Moon, and W. Jung, “Lamellar keratoplasty using position-guided surgical needle and M-mode optical coherence tomography,” J. Biomed. Opt. 22, 125005 (2017).
[Crossref]

Clausi, D.

J. Eichel, A. Mishra, P. Fieguth, D. Clausi, and K. Bizheva, “A novel algorithm for extraction of the layers of the cornea,” in Canadian Conference of Computer and Robot Vision, (IEEE, 2009), pp. 313–320.

Cole, E.

T. B. DuBose, D. Cunefare, E. Cole, P. Milanfar, J. A. Izatt, and S. Farsiu, “Statistical models of signal and noise and fundamental limits of segmentation accuracy in retinal optical coherence tomography,” IEEE Trans. Med. Imag. (2017).
[Crossref]

Colin, J.

D. Smadja, J. Colin, R. R. Krueger, G. R. Mello, A. Gallois, B. Mortemousque, and D. Touboul, “Outcomes of deep anterior lamellar keratoplasty for keratoconus: learning curve and advantages of the big bubble technique,” Cornea 31, 859–863 (2012).
[Crossref] [PubMed]

Cua, M.

J. Xu, K. S. Wong, V. Wong, M. Heisler, S. Lee, M. Cua, Y. Jian, and M. V. Sarunic, “Enhancing the visualization of human retina vascular networks by graphics processing unit accelerated speckle variance OCT and graph cut retinal layer segmentation,” Proc. SPIE 9132, 93122H (2015).

Cunefare, D.

L. Fang, D. Cunefare, C. Wang, R. H. Guymer, S. Li, and S. Farsiu, “Automatic segmentation of nine retinal layer boundaries in oct images of non-exudative amd patients using deep learning and graph search,” Biomed. Opt. Express 8, 2732–2744 (2017).
[Crossref] [PubMed]

B. Keller, D. Cunefare, D. S. Grewal, T. H. Mahmoud, J. A. Izatt, and S. Farsiu, “Length-adaptive graph search for automatic segmentation of pathological features in optical coherence tomography images,” J. Biomed. Opt. 21, 076015 (2016).
[Crossref]

N. D. Pasricha, C. Shieh, O. M. Carrasco-Zevallos, B. Keller, D. Cunefare, J. S. Mehta, S. Farsiu, J. A. Izatt, C. A. Toth, and A. N. Kuo, “Needle depth and big-bubble success in deep anterior lamellar keratoplasty: An ex vivo microscope-integrated OCT study,” Cornea 35, 1471–1477 (2016).
[Crossref] [PubMed]

P. Hahn, O. Carrasco-Zevallos, D. Cunefare, J. Migacz, S. Farsiu, J. A. Izatt, and C. A. Toth, “Intrasurgical human retinal imaging with manual instrument tracking using a microscope-integrated spectral-domain optical coherence tomography device,” Translational Vision Science & Technology 4, 1 (2015).
[Crossref]

C. Viehland, B. Keller, O. Carrasco-Zevallos, D. Cunefare, L. Shen, C. Toth, S. Farsiu, and J. A. Izatt, “Novel real-time volumetric tool segmentation algorithm for intraoperative microscope integrated OCT,” in SPIE BiOS (International Society for Optics and Photonics, 2016), p. 969702.

T. B. DuBose, D. Cunefare, E. Cole, P. Milanfar, J. A. Izatt, and S. Farsiu, “Statistical models of signal and noise and fundamental limits of segmentation accuracy in retinal optical coherence tomography,” IEEE Trans. Med. Imag. (2017).
[Crossref]

Cursiefen, C.

S. Siebelmann, P. Steven, D. Hos, G. Hüttmann, E. Lankenau, B. Bachmann, and C. Cursiefen, “Advantages of microscope-integrated intraoperative online optical coherence tomography: usage in boston keratoprosthesis type I surgery,” J. Biomed. Opt. 21, 016005 (2016).
[Crossref]

P. Steven, C. Le Blanc, E. Lankenau, M. Krug, S. Oelckers, L. M. Heindl, U. Gehlsen, G. Huettmann, and C. Cursiefen, “Optimising deep anterior lamellar keratoplasty (DALK) using intraoperative online optical coherence tomography (iOCT),” Br. J. Ophthalmol. 98, 900–904 (2014).
[Crossref] [PubMed]

P. Steven, C. Le Blanc, K. Velten, E. Lankenau, M. Krug, S. Oelckers, L. M. Heindl, U. Gehlsen, G. Hüttmann, and C. Cursiefen, “Optimizing descemet membrane endothelial keratoplasty using intraoperative optical coherence tomography,” JAMA Ophthalmology 131, 1135–1142 (2013).
[Crossref] [PubMed]

Davey, P. G.

D. Williams, Y. Zheng, P. G. Davey, F. Bao, M. Shen, and A. Elsheikh, “Reconstruction of 3D surface maps from anterior segment optical coherence tomography images using graph theory and genetic algorithms,” Biomed. Signal Process. Control 25, 91–98 (2016).
[Crossref]

Dayani, P. N.

P. N. Dayani, R. Maldonado, S. Farsiu, and C. A. Toth, “Intraoperative use of handheld spectral domain optical coherence tomography imaging in macular surgery,” Retina 29, 1457 (2009).
[Crossref] [PubMed]

de Boor, C.

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V. M. Borderie, O. Sandali, J. Bullet, T. Gaujoux, O. Touzeau, and L. Laroche, “Long-term results of deep anterior lamellar versus penetrating keratoplasty,” Ophthalmology 119, 249–255 (2012).
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J. Tian, B. Varga, G. M. Somfai, W.-H. Lee, W. E. Smiddy, and D. C. DeBuc, “Real-time automatic segmentation of optical coherence tomography volume data of the macular region,” PloS One 10, e0133908 (2015).
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Opt. Express (4)

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Figures (9)

Fig. 1
Fig. 1 Flow chart of segmentation and tracking to find needle penetration depth. The acquisition software acquired volumes of 96 B-scans in three groups of 32 B-scans. B-scan segmentation of each group occurred during the acquisition of the next group.
Fig. 2
Fig. 2 Illustration of corneal segmentation. (A) Original image obtained from human cadaver corneal sample. (B) Epithelial segmentation (orange) with epithelial constraints (magenta). (C) Epithelial and endothelial segmentation (orange) with endothelial constraints (magenta).
Fig. 3
Fig. 3 Representation of the process used to determine an estimate of the needle base and tip. (A) DC-subtracted maximum intensity projection (MIP). (B) Thresholded depth map. (C) Six largest connected components from the depth map. Only the green connected component fit the width criterion. (D) Needle base estimate (green circle) and needle tip estimate (red circle) based on the intersection of the line formed by the first principal component with the borders of the image (blue line). Pixels identified as the needle are orange. Best viewed in color.
Fig. 4
Fig. 4 Example needle shadow segmentation correction. (A) B-scan with uncorrected segmentation. Shadows from the needle interfere with the endothelial surface segmentation. Inflating the needle allows for the area by the white arrow to be corrected. (B) Corrected segmentation taken from height map in (F). (C) Height map of the endothelial surface of the original segmentation. Black arrow denotes corrupted segmentation caused by the needle. (D) Height map of the endothelial surface of the original segmentation with the inflated needle pixels marked in green and the location of B-scan (A) and (B) denoted by the blue line. (E) Height map of the endothelial surface of the original segmentation with pixels that changed after the trial inpainting marked in green. (F) Corrected height map after inpainting green pixels in (E). Black arrow denotes original location of corrupted segmentation.
Fig. 5
Fig. 5 Refraction corrected cross section along the axis of the needle. Green dots denote the epithelial surface point, needle tip, and endothelial surface point used to compute the depth along the magenta line.
Fig. 6
Fig. 6 Experimental setup for validation experiments. In the experiment where corneal fellows inserted needles into the cornea, a tracked cross section was displayed on the monitor next to the microscope.
Fig. 7
Fig. 7 Series of images depicting different needle penetration depths, as shown to all surgeons prior to performing the experiment. Needle percent depths are displayed at the bottom of each image.
Fig. 8
Fig. 8 Comparison of manual and automatic segmentation for a B-scan with and without a needle. (A) Original B-scan, with no needle. (B) Segmented B-scan. Green denotes the manual segmentation and purple denotes the automatic segmentation. Where the green is not visible, the two methods segmented the same point. (C) Original B-scan with a needle. (D) Uncorrected automatic segmentation. (E) Corrected automatic segmentation (purple) and manual segmentation (green). Best viewed in color.
Fig. 9
Fig. 9 (A) Plot of the final needle depth expressed as a percent of corneal thickness for all trials in which the surgeon did not puncture the endothelium. A blue X indicates the mean of the group and error bars denote one standard deviation. (B) Plot illustrating performance of the automatic needle percent depth calculation compared to the manual calculation.

Tables (2)

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Table 1 Mean Absolute A-scan Segmentation Error

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Table 2 Needle Tracking Position and Rotation Error

Equations (1)

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w i j = d i j ( 2 ( G i + G j ) + 10 5 )

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