Abstract

Doppler optical coherence tomography (OCT) imaging of vessels after anastomosis procedures can provide high resolution structure and blood flow imaging of the vessel simultaneously for objective surgical evaluation. Automatic boundary segmentation of the outer vessel wall boundary and its inner lumen contour is a very crucial and fundamental step for the responsive and complicated quantitative analysis required in future clinical applications of Doppler OCT imaging. In this work, we proposed a cascaded U-net (CU-net) architecture to segment the vascular intensity image and its corresponding phase image for the outer vessel wall boundary and the inner blood flowing lumen contour, respectively. CU-net architecture was developed by training two specific U-net frameworks in coordination: the first performs intensity image segmentation while the second performs phase image segmentation. Output of the first framework was sent to the input of the second framework as a mask to select area of interests. Model training time can be reduced effectively by cascading two U-net frameworks. Testing segmentation accuracy for outer vessel wall boundary and inner lumen contour were calculated to be 96.7%±0.2% and 94.8%±0.2%, respectively. The CU-net architecture requires no pre-processing on noises inherent with OCT images, such as random noise and speckle noise. The segmentation was automatic and end-to-end. 250 Doppler OCT images from one in-vivo mouse femoral artery imaging were successfully segmented automatically with an average processing time of 0.68s including both the outer vessel boundary and inner lumen contour. Thrombosis morphology, the inner blood flowing lumen area, and its radius variation were quantitatively analyzed based on the segmentation results, demonstrating the potential for clinical objective evaluation.

© 2019 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

Full Article  |  PDF Article
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References

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2019 (1)

G. N. Girish, T. Bibhash, R. Sohini, R. Abhishek, and R. Jeny, “Segmentation of Intra-Retinal Cysts from Optical Coherence Tomography Images using a Fully Convolutional Neural Network Model,” IEEE J Biomed. Health Inform. 23(1), 296–304 (2019).
[Crossref]

2018 (5)

2017 (6)

2016 (1)

A. Guha Roy, S. Conjeti, S. G. Carlier, P. K. Dutta, A. Kastrati, A. F. Laine, N. Navab, A. Katouzian, and D. Sheet, “Lumen Segmentation in Intravascular Optical Coherence Tomography Using Backscattering Tracked and Initialized Random Walks,” IEEE J Biomed. Health Inform. 20(2), 606–614 (2016).
[Crossref]

2015 (2)

Y. Huang, D. Tong, S. Zhu, L. Wu, Q. Mao, Z. Ibrahim, W. P. Lee, G. Brandacher, and J. U. Kang, “Evaluation of microvascular anastomosis using real-time, ultra-high-resolution, Fourier domain Doppler optical coherence tomography,” Plast. Reconstr. Surg. 135(4), 711e–720e (2015).
[Crossref]

Y. Mei, M. Müller-Eschner, J. Yi, Z. Zhang, D. Chen, M. Kronlage, H. Tengg-Kobligk, H. U. Kauczor, D. Böckler, and S. Demirel, “Hemodynamics analyses in treated and untreated carotid arteries of the same patient: a preliminary study based on three patientcases,” Bio-Med. Mater. Eng. 26(s1), S299–S309 (2015).
[Crossref]

2014 (2)

L. Aida, “Alexis Carrel (1873–1944): Visionary vascular surgeon and pioneer in organ transplantation,” J. Med. Biogr. 22(3), 172–175 (2014).
[Crossref]

Y. Huang, G. J. Furtmüller, D. Tong, S. Zhu, W. P. Lee, G. Brandacher, and J. U. Kang, “MEMS-based handheld fourier domain Doppler optical coherence tomography for intraoperative microvascular anastomosis imaging,” PLoS One 9(12), e114215 (2014).
[Crossref]

2013 (2)

Y. Huang, Z. Ibrahim, D. Tong, S. Zhu, Q. Mao, J. Pang, W. P. Andree Lee, G. Brandacher, and J. U. Kang, “Microvascular anastomosis guidance and evaluation using real-time three-dimensional Fourier-domain Doppler optical coherence tomography,” J. Biomed. Opt. 18(11), 111404 (2013).
[Crossref]

L. Fang, S. Li, R. P. McNabb, Q. Nie, A. N. Kuo, C. A. Toth, J. A. Izatt, and S. Farsiu, “Fast acquisition and reconstruction of optical coherence tomography images via sparse representation,” IEEE Trans. Med. Imaging 32(11), 2034–2049 (2013).
[Crossref]

2012 (1)

A. Liu, X. Yin, L. Shi, P. Li, K. L. Thornburg, R. K. Wang, and S. Rugonyi, “Biomechanics of the Chick Embryonic Heart Outflow Tract at HH18 Using 4D Optical Coherence TomographyImaging and Computational odeling,” PLoS One 7(7), e40869 (2012).
[Crossref]

2011 (2)

E. I. Chang, M. G. Galvez, J. P. Glotzbach, C. D. Hamou, S. El-fesi, C. T. Rappleye, K. M. Sommer, J. Rajadas, O. J. Abilez, G. G. Fuller, M. T. Longaker, and G. C. Gurtner, “Vascular anastomosis using controlled phase transitions in poloxamer gels,” Nat. Med. 17(9), 1147–1152 (2011).
[Crossref]

A. Yazdanpanah, G. Hamar, B. R. Smith, and M. V. Sarunic, “Segmentation of intra-retinal layers from optical coherence tomgraphy images using an active contour approach,” IEEE Trans. Med. Imaging 30(2), 484–496 (2011).
[Crossref]

2010 (1)

F. M. Leclère, M. Schoofs, F. Auger, B. Buys, and S. R. Mordon, “Blood Flow Assessment with Magnetic Resonance Imaging After 1.9 mm Diode Laser-Assisted Microvascular Anastomosis,” Lasers Surg. Med. 42(4), 299–305 (2010).
[Crossref]

2009 (1)

2008 (1)

2006 (1)

L. Grady, “Random walks for image segmentation,” IEEE Trans. Pattern Anal. Mach. Intell. 28(11), 1768–1783 (2006).
[Crossref]

2005 (1)

D. Tang, C. Yang, J. Zheng, P. K. Woodard, J. E. Saffitz, J. D. Petruccelli, G. A. Sicard, and C. Yuan, “Local Maximal Stress Hypothesis and Computational Plaque Vulnerability Index for Atherosclerotic Plaque Assessment,” Ann. Biomed. Eng. 33(12), 1789–1801 (2005).
[Crossref]

1998 (1)

S. A. Boppart, B. E. Bouma, C. Pitris, G. J. Tearny, and J. F. Southern, “Intraoperative assessment of microsurgery with three-dimensional optical coherence tomography,” Radiology 208(1), 81–86 (1998).
[Crossref]

1997 (1)

Abhishek, R.

G. N. Girish, T. Bibhash, R. Sohini, R. Abhishek, and R. Jeny, “Segmentation of Intra-Retinal Cysts from Optical Coherence Tomography Images using a Fully Convolutional Neural Network Model,” IEEE J Biomed. Health Inform. 23(1), 296–304 (2019).
[Crossref]

Abilez, O. J.

E. I. Chang, M. G. Galvez, J. P. Glotzbach, C. D. Hamou, S. El-fesi, C. T. Rappleye, K. M. Sommer, J. Rajadas, O. J. Abilez, G. G. Fuller, M. T. Longaker, and G. C. Gurtner, “Vascular anastomosis using controlled phase transitions in poloxamer gels,” Nat. Med. 17(9), 1147–1152 (2011).
[Crossref]

Abramoff, M. D.

Ahmadi, S. A.

P. F. Christ, M. E. A. Elshaer, F. Ettlinger, S. Tatavarty, M. Bickel, P. Bilic, M. Rempfler, M. Armbruster, F. Hofmann, M. D’Anastasi, W. H. Sommer, S. A. Ahmadi, and B. H. Menze, “Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields,” arXiv:1610.02177 [cs.CV] (2016).

Ai, D.

J. Fan, J. Yang, Y. Wang, S. Yang, D. Ai, Y. Huang, H. Song, A. Hao, and Y. Wang, “Multichannel Fully Convolutional Network for Coronary Artery Segmentation in X-ray Angiograms,” IEEE Access 6, 44635–44643 (2018).
[Crossref]

J. Zhao, J. Yang, D. Ai, H. Song, Y. Jiang, Y. Huang, L. Zhang, and Y. Wang, “Automatic retinal vessel segmentation using multi-scale superpixel chain tracking,” Digital Signal Processing 81, 26–42 (2018).
[Crossref]

Aida, L.

L. Aida, “Alexis Carrel (1873–1944): Visionary vascular surgeon and pioneer in organ transplantation,” J. Med. Biogr. 22(3), 172–175 (2014).
[Crossref]

Alonso-Caneiro, D.

Andree Lee, W. P.

Y. Huang, Z. Ibrahim, D. Tong, S. Zhu, Q. Mao, J. Pang, W. P. Andree Lee, G. Brandacher, and J. U. Kang, “Microvascular anastomosis guidance and evaluation using real-time three-dimensional Fourier-domain Doppler optical coherence tomography,” J. Biomed. Opt. 18(11), 111404 (2013).
[Crossref]

Armbruster, M.

P. F. Christ, M. E. A. Elshaer, F. Ettlinger, S. Tatavarty, M. Bickel, P. Bilic, M. Rempfler, M. Armbruster, F. Hofmann, M. D’Anastasi, W. H. Sommer, S. A. Ahmadi, and B. H. Menze, “Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields,” arXiv:1610.02177 [cs.CV] (2016).

Auger, F.

F. M. Leclère, M. Schoofs, F. Auger, B. Buys, and S. R. Mordon, “Blood Flow Assessment with Magnetic Resonance Imaging After 1.9 mm Diode Laser-Assisted Microvascular Anastomosis,” Lasers Surg. Med. 42(4), 299–305 (2010).
[Crossref]

Ba, J.

D. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint 1412.6980 (2014).

Bibhash, T.

G. N. Girish, T. Bibhash, R. Sohini, R. Abhishek, and R. Jeny, “Segmentation of Intra-Retinal Cysts from Optical Coherence Tomography Images using a Fully Convolutional Neural Network Model,” IEEE J Biomed. Health Inform. 23(1), 296–304 (2019).
[Crossref]

Bickel, M.

P. F. Christ, M. E. A. Elshaer, F. Ettlinger, S. Tatavarty, M. Bickel, P. Bilic, M. Rempfler, M. Armbruster, F. Hofmann, M. D’Anastasi, W. H. Sommer, S. A. Ahmadi, and B. H. Menze, “Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields,” arXiv:1610.02177 [cs.CV] (2016).

Bilic, P.

P. F. Christ, M. E. A. Elshaer, F. Ettlinger, S. Tatavarty, M. Bickel, P. Bilic, M. Rempfler, M. Armbruster, F. Hofmann, M. D’Anastasi, W. H. Sommer, S. A. Ahmadi, and B. H. Menze, “Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields,” arXiv:1610.02177 [cs.CV] (2016).

Bizheva, K.

Böckler, D.

Y. Mei, M. Müller-Eschner, J. Yi, Z. Zhang, D. Chen, M. Kronlage, H. Tengg-Kobligk, H. U. Kauczor, D. Böckler, and S. Demirel, “Hemodynamics analyses in treated and untreated carotid arteries of the same patient: a preliminary study based on three patientcases,” Bio-Med. Mater. Eng. 26(s1), S299–S309 (2015).
[Crossref]

Boppart, S. A.

S. A. Boppart, B. E. Bouma, C. Pitris, G. J. Tearny, and J. F. Southern, “Intraoperative assessment of microsurgery with three-dimensional optical coherence tomography,” Radiology 208(1), 81–86 (1998).
[Crossref]

Bouma, B. E.

S. A. Boppart, B. E. Bouma, C. Pitris, G. J. Tearny, and J. F. Southern, “Intraoperative assessment of microsurgery with three-dimensional optical coherence tomography,” Radiology 208(1), 81–86 (1998).
[Crossref]

Brandacher, G.

Y. Huang, D. Tong, S. Zhu, L. Wu, Q. Mao, Z. Ibrahim, W. P. Lee, G. Brandacher, and J. U. Kang, “Evaluation of microvascular anastomosis using real-time, ultra-high-resolution, Fourier domain Doppler optical coherence tomography,” Plast. Reconstr. Surg. 135(4), 711e–720e (2015).
[Crossref]

Y. Huang, G. J. Furtmüller, D. Tong, S. Zhu, W. P. Lee, G. Brandacher, and J. U. Kang, “MEMS-based handheld fourier domain Doppler optical coherence tomography for intraoperative microvascular anastomosis imaging,” PLoS One 9(12), e114215 (2014).
[Crossref]

Y. Huang, Z. Ibrahim, D. Tong, S. Zhu, Q. Mao, J. Pang, W. P. Andree Lee, G. Brandacher, and J. U. Kang, “Microvascular anastomosis guidance and evaluation using real-time three-dimensional Fourier-domain Doppler optical coherence tomography,” J. Biomed. Opt. 18(11), 111404 (2013).
[Crossref]

Brox, T.

O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional Networks for Biomedical Image S Microvascular egmentation,” arXiv: 1505.04597v1 [cs.CV] (2015).

Buys, B.

F. M. Leclère, M. Schoofs, F. Auger, B. Buys, and S. R. Mordon, “Blood Flow Assessment with Magnetic Resonance Imaging After 1.9 mm Diode Laser-Assisted Microvascular Anastomosis,” Lasers Surg. Med. 42(4), 299–305 (2010).
[Crossref]

Carlier, S. G.

A. Guha Roy, S. Conjeti, S. G. Carlier, P. K. Dutta, A. Kastrati, A. F. Laine, N. Navab, A. Katouzian, and D. Sheet, “Lumen Segmentation in Intravascular Optical Coherence Tomography Using Backscattering Tracked and Initialized Random Walks,” IEEE J Biomed. Health Inform. 20(2), 606–614 (2016).
[Crossref]

Chang, E. I.

E. I. Chang, M. G. Galvez, J. P. Glotzbach, C. D. Hamou, S. El-fesi, C. T. Rappleye, K. M. Sommer, J. Rajadas, O. J. Abilez, G. G. Fuller, M. T. Longaker, and G. C. Gurtner, “Vascular anastomosis using controlled phase transitions in poloxamer gels,” Nat. Med. 17(9), 1147–1152 (2011).
[Crossref]

Chen, D.

Y. Mei, M. Müller-Eschner, J. Yi, Z. Zhang, D. Chen, M. Kronlage, H. Tengg-Kobligk, H. U. Kauczor, D. Böckler, and S. Demirel, “Hemodynamics analyses in treated and untreated carotid arteries of the same patient: a preliminary study based on three patientcases,” Bio-Med. Mater. Eng. 26(s1), S299–S309 (2015).
[Crossref]

Chen, H.

Y. Lequan, H. Chen, Q. Dou, J. Qin, and P. A. Heng, “Automated melanoma recognition in dermoscopy images via very deep residual networks,” IEEE Trans. Med. Imag. 36(4), 994–1004 (2017).
[Crossref]

Chen, L.

K. Kamnitsas, L. Chen, C. Ledig, D. Rueckert, and B. Glocker, “Multiscale 3D convolutional neural networks for lesion segmentation in brain MRI,” in Proc of MICCAI Brain Lesion Workshop (2015).

Christ, P. F.

P. F. Christ, M. E. A. Elshaer, F. Ettlinger, S. Tatavarty, M. Bickel, P. Bilic, M. Rempfler, M. Armbruster, F. Hofmann, M. D’Anastasi, W. H. Sommer, S. A. Ahmadi, and B. H. Menze, “Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields,” arXiv:1610.02177 [cs.CV] (2016).

Clausi, D. A.

Collins, M. J.

Conjeti, S.

A. G. Roy, S. Conjeti, S. P. K. Karri, D. Sheet, A. Katouzian, C. Wachinger, and N. Navab, “ReLayNet: retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networks,” Biomed. Opt. Express 8(8), 3627–3642 (2017).
[Crossref]

A. Guha Roy, S. Conjeti, S. G. Carlier, P. K. Dutta, A. Kastrati, A. F. Laine, N. Navab, A. Katouzian, and D. Sheet, “Lumen Segmentation in Intravascular Optical Coherence Tomography Using Backscattering Tracked and Initialized Random Walks,” IEEE J Biomed. Health Inform. 20(2), 606–614 (2016).
[Crossref]

Cunefare, D.

D’Anastasi, M.

P. F. Christ, M. E. A. Elshaer, F. Ettlinger, S. Tatavarty, M. Bickel, P. Bilic, M. Rempfler, M. Armbruster, F. Hofmann, M. D’Anastasi, W. H. Sommer, S. A. Ahmadi, and B. H. Menze, “Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields,” arXiv:1610.02177 [cs.CV] (2016).

Darrell, T.

J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition3431–3440 (2015).
[Crossref]

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Rokem, A.

Ronneberger, O.

O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional Networks for Biomedical Image S Microvascular egmentation,” arXiv: 1505.04597v1 [cs.CV] (2015).

Roy, A. G.

Rueckert, D.

K. Kamnitsas, L. Chen, C. Ledig, D. Rueckert, and B. Glocker, “Multiscale 3D convolutional neural networks for lesion segmentation in brain MRI,” in Proc of MICCAI Brain Lesion Workshop (2015).

Rugonyi, S.

A. Liu, X. Yin, L. Shi, P. Li, K. L. Thornburg, R. K. Wang, and S. Rugonyi, “Biomechanics of the Chick Embryonic Heart Outflow Tract at HH18 Using 4D Optical Coherence TomographyImaging and Computational odeling,” PLoS One 7(7), e40869 (2012).
[Crossref]

Saffitz, J. E.

D. Tang, C. Yang, J. Zheng, P. K. Woodard, J. E. Saffitz, J. D. Petruccelli, G. A. Sicard, and C. Yuan, “Local Maximal Stress Hypothesis and Computational Plaque Vulnerability Index for Atherosclerotic Plaque Assessment,” Ann. Biomed. Eng. 33(12), 1789–1801 (2005).
[Crossref]

Sanchez, C. I.

Sarunic, M. V.

A. Yazdanpanah, G. Hamar, B. R. Smith, and M. V. Sarunic, “Segmentation of intra-retinal layers from optical coherence tomgraphy images using an active contour approach,” IEEE Trans. Med. Imaging 30(2), 484–496 (2011).
[Crossref]

Schoofs, M.

F. M. Leclère, M. Schoofs, F. Auger, B. Buys, and S. R. Mordon, “Blood Flow Assessment with Magnetic Resonance Imaging After 1.9 mm Diode Laser-Assisted Microvascular Anastomosis,” Lasers Surg. Med. 42(4), 299–305 (2010).
[Crossref]

Schreur, V.

Shah, A.

Sheet, D.

A. G. Roy, S. Conjeti, S. P. K. Karri, D. Sheet, A. Katouzian, C. Wachinger, and N. Navab, “ReLayNet: retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networks,” Biomed. Opt. Express 8(8), 3627–3642 (2017).
[Crossref]

A. Guha Roy, S. Conjeti, S. G. Carlier, P. K. Dutta, A. Kastrati, A. F. Laine, N. Navab, A. Katouzian, and D. Sheet, “Lumen Segmentation in Intravascular Optical Coherence Tomography Using Backscattering Tracked and Initialized Random Walks,” IEEE J Biomed. Health Inform. 20(2), 606–614 (2016).
[Crossref]

Shelhamer, E.

J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition3431–3440 (2015).
[Crossref]

Shi, L.

A. Liu, X. Yin, L. Shi, P. Li, K. L. Thornburg, R. K. Wang, and S. Rugonyi, “Biomechanics of the Chick Embryonic Heart Outflow Tract at HH18 Using 4D Optical Coherence TomographyImaging and Computational odeling,” PLoS One 7(7), e40869 (2012).
[Crossref]

Sicard, G. A.

D. Tang, C. Yang, J. Zheng, P. K. Woodard, J. E. Saffitz, J. D. Petruccelli, G. A. Sicard, and C. Yuan, “Local Maximal Stress Hypothesis and Computational Plaque Vulnerability Index for Atherosclerotic Plaque Assessment,” Ann. Biomed. Eng. 33(12), 1789–1801 (2005).
[Crossref]

Smith, B. R.

A. Yazdanpanah, G. Hamar, B. R. Smith, and M. V. Sarunic, “Segmentation of intra-retinal layers from optical coherence tomgraphy images using an active contour approach,” IEEE Trans. Med. Imaging 30(2), 484–496 (2011).
[Crossref]

Sohini, R.

G. N. Girish, T. Bibhash, R. Sohini, R. Abhishek, and R. Jeny, “Segmentation of Intra-Retinal Cysts from Optical Coherence Tomography Images using a Fully Convolutional Neural Network Model,” IEEE J Biomed. Health Inform. 23(1), 296–304 (2019).
[Crossref]

Sommer, K. M.

E. I. Chang, M. G. Galvez, J. P. Glotzbach, C. D. Hamou, S. El-fesi, C. T. Rappleye, K. M. Sommer, J. Rajadas, O. J. Abilez, G. G. Fuller, M. T. Longaker, and G. C. Gurtner, “Vascular anastomosis using controlled phase transitions in poloxamer gels,” Nat. Med. 17(9), 1147–1152 (2011).
[Crossref]

Sommer, W. H.

P. F. Christ, M. E. A. Elshaer, F. Ettlinger, S. Tatavarty, M. Bickel, P. Bilic, M. Rempfler, M. Armbruster, F. Hofmann, M. D’Anastasi, W. H. Sommer, S. A. Ahmadi, and B. H. Menze, “Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields,” arXiv:1610.02177 [cs.CV] (2016).

Song, H.

J. Fan, J. Yang, Y. Wang, S. Yang, D. Ai, Y. Huang, H. Song, A. Hao, and Y. Wang, “Multichannel Fully Convolutional Network for Coronary Artery Segmentation in X-ray Angiograms,” IEEE Access 6, 44635–44643 (2018).
[Crossref]

J. Zhao, J. Yang, D. Ai, H. Song, Y. Jiang, Y. Huang, L. Zhang, and Y. Wang, “Automatic retinal vessel segmentation using multi-scale superpixel chain tracking,” Digital Signal Processing 81, 26–42 (2018).
[Crossref]

Southern, J. F.

S. A. Boppart, B. E. Bouma, C. Pitris, G. J. Tearny, and J. F. Southern, “Intraoperative assessment of microsurgery with three-dimensional optical coherence tomography,” Radiology 208(1), 81–86 (1998).
[Crossref]

Tang, D.

D. Tang, C. Yang, J. Zheng, P. K. Woodard, J. E. Saffitz, J. D. Petruccelli, G. A. Sicard, and C. Yuan, “Local Maximal Stress Hypothesis and Computational Plaque Vulnerability Index for Atherosclerotic Plaque Assessment,” Ann. Biomed. Eng. 33(12), 1789–1801 (2005).
[Crossref]

Tatavarty, S.

P. F. Christ, M. E. A. Elshaer, F. Ettlinger, S. Tatavarty, M. Bickel, P. Bilic, M. Rempfler, M. Armbruster, F. Hofmann, M. D’Anastasi, W. H. Sommer, S. A. Ahmadi, and B. H. Menze, “Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields,” arXiv:1610.02177 [cs.CV] (2016).

Tearny, G. J.

S. A. Boppart, B. E. Bouma, C. Pitris, G. J. Tearny, and J. F. Southern, “Intraoperative assessment of microsurgery with three-dimensional optical coherence tomography,” Radiology 208(1), 81–86 (1998).
[Crossref]

Tengg-Kobligk, H.

Y. Mei, M. Müller-Eschner, J. Yi, Z. Zhang, D. Chen, M. Kronlage, H. Tengg-Kobligk, H. U. Kauczor, D. Böckler, and S. Demirel, “Hemodynamics analyses in treated and untreated carotid arteries of the same patient: a preliminary study based on three patientcases,” Bio-Med. Mater. Eng. 26(s1), S299–S309 (2015).
[Crossref]

Theelen, T.

Thornburg, K. L.

A. Liu, X. Yin, L. Shi, P. Li, K. L. Thornburg, R. K. Wang, and S. Rugonyi, “Biomechanics of the Chick Embryonic Heart Outflow Tract at HH18 Using 4D Optical Coherence TomographyImaging and Computational odeling,” PLoS One 7(7), e40869 (2012).
[Crossref]

Thurman, S. T.

Tong, D.

Y. Huang, D. Tong, S. Zhu, L. Wu, Q. Mao, Z. Ibrahim, W. P. Lee, G. Brandacher, and J. U. Kang, “Evaluation of microvascular anastomosis using real-time, ultra-high-resolution, Fourier domain Doppler optical coherence tomography,” Plast. Reconstr. Surg. 135(4), 711e–720e (2015).
[Crossref]

Y. Huang, G. J. Furtmüller, D. Tong, S. Zhu, W. P. Lee, G. Brandacher, and J. U. Kang, “MEMS-based handheld fourier domain Doppler optical coherence tomography for intraoperative microvascular anastomosis imaging,” PLoS One 9(12), e114215 (2014).
[Crossref]

Y. Huang, Z. Ibrahim, D. Tong, S. Zhu, Q. Mao, J. Pang, W. P. Andree Lee, G. Brandacher, and J. U. Kang, “Microvascular anastomosis guidance and evaluation using real-time three-dimensional Fourier-domain Doppler optical coherence tomography,” J. Biomed. Opt. 18(11), 111404 (2013).
[Crossref]

Toth, C. A.

L. Fang, S. Li, R. P. McNabb, Q. Nie, A. N. Kuo, C. A. Toth, J. A. Izatt, and S. Farsiu, “Fast acquisition and reconstruction of optical coherence tomography images via sparse representation,” IEEE Trans. Med. Imaging 32(11), 2034–2049 (2013).
[Crossref]

Tyring, A. J.

van Asten, F.

van Ginneken, B.

Venhuizen, F. G.

Vincent, S. J.

Wachinger, C.

Wang, C.

Wang, R. K.

A. Liu, X. Yin, L. Shi, P. Li, K. L. Thornburg, R. K. Wang, and S. Rugonyi, “Biomechanics of the Chick Embryonic Heart Outflow Tract at HH18 Using 4D Optical Coherence TomographyImaging and Computational odeling,” PLoS One 7(7), e40869 (2012).
[Crossref]

Wang, Y.

J. Fan, J. Yang, Y. Wang, S. Yang, D. Ai, Y. Huang, H. Song, A. Hao, and Y. Wang, “Multichannel Fully Convolutional Network for Coronary Artery Segmentation in X-ray Angiograms,” IEEE Access 6, 44635–44643 (2018).
[Crossref]

J. Fan, J. Yang, Y. Wang, S. Yang, D. Ai, Y. Huang, H. Song, A. Hao, and Y. Wang, “Multichannel Fully Convolutional Network for Coronary Artery Segmentation in X-ray Angiograms,” IEEE Access 6, 44635–44643 (2018).
[Crossref]

J. Zhao, J. Yang, D. Ai, H. Song, Y. Jiang, Y. Huang, L. Zhang, and Y. Wang, “Automatic retinal vessel segmentation using multi-scale superpixel chain tracking,” Digital Signal Processing 81, 26–42 (2018).
[Crossref]

Wong, A.

Woodard, P. K.

D. Tang, C. Yang, J. Zheng, P. K. Woodard, J. E. Saffitz, J. D. Petruccelli, G. A. Sicard, and C. Yuan, “Local Maximal Stress Hypothesis and Computational Plaque Vulnerability Index for Atherosclerotic Plaque Assessment,” Ann. Biomed. Eng. 33(12), 1789–1801 (2005).
[Crossref]

Wu, L.

Y. Huang, D. Tong, S. Zhu, L. Wu, Q. Mao, Z. Ibrahim, W. P. Lee, G. Brandacher, and J. U. Kang, “Evaluation of microvascular anastomosis using real-time, ultra-high-resolution, Fourier domain Doppler optical coherence tomography,” Plast. Reconstr. Surg. 135(4), 711e–720e (2015).
[Crossref]

Wu, X.

Wu, Y.

Yang, C.

D. Tang, C. Yang, J. Zheng, P. K. Woodard, J. E. Saffitz, J. D. Petruccelli, G. A. Sicard, and C. Yuan, “Local Maximal Stress Hypothesis and Computational Plaque Vulnerability Index for Atherosclerotic Plaque Assessment,” Ann. Biomed. Eng. 33(12), 1789–1801 (2005).
[Crossref]

Yang, J.

J. Fan, J. Yang, Y. Wang, S. Yang, D. Ai, Y. Huang, H. Song, A. Hao, and Y. Wang, “Multichannel Fully Convolutional Network for Coronary Artery Segmentation in X-ray Angiograms,” IEEE Access 6, 44635–44643 (2018).
[Crossref]

J. Zhao, J. Yang, D. Ai, H. Song, Y. Jiang, Y. Huang, L. Zhang, and Y. Wang, “Automatic retinal vessel segmentation using multi-scale superpixel chain tracking,” Digital Signal Processing 81, 26–42 (2018).
[Crossref]

Yang, S.

J. Fan, J. Yang, Y. Wang, S. Yang, D. Ai, Y. Huang, H. Song, A. Hao, and Y. Wang, “Multichannel Fully Convolutional Network for Coronary Artery Segmentation in X-ray Angiograms,” IEEE Access 6, 44635–44643 (2018).
[Crossref]

Yazdanpanah, A.

A. Yazdanpanah, G. Hamar, B. R. Smith, and M. V. Sarunic, “Segmentation of intra-retinal layers from optical coherence tomgraphy images using an active contour approach,” IEEE Trans. Med. Imaging 30(2), 484–496 (2011).
[Crossref]

Yi, F.

Yi, J.

Y. Mei, M. Müller-Eschner, J. Yi, Z. Zhang, D. Chen, M. Kronlage, H. Tengg-Kobligk, H. U. Kauczor, D. Böckler, and S. Demirel, “Hemodynamics analyses in treated and untreated carotid arteries of the same patient: a preliminary study based on three patientcases,” Bio-Med. Mater. Eng. 26(s1), S299–S309 (2015).
[Crossref]

Yin, X.

A. Liu, X. Yin, L. Shi, P. Li, K. L. Thornburg, R. K. Wang, and S. Rugonyi, “Biomechanics of the Chick Embryonic Heart Outflow Tract at HH18 Using 4D Optical Coherence TomographyImaging and Computational odeling,” PLoS One 7(7), e40869 (2012).
[Crossref]

Yuan, C.

D. Tang, C. Yang, J. Zheng, P. K. Woodard, J. E. Saffitz, J. D. Petruccelli, G. A. Sicard, and C. Yuan, “Local Maximal Stress Hypothesis and Computational Plaque Vulnerability Index for Atherosclerotic Plaque Assessment,” Ann. Biomed. Eng. 33(12), 1789–1801 (2005).
[Crossref]

Zhang, L.

J. Zhao, J. Yang, D. Ai, H. Song, Y. Jiang, Y. Huang, L. Zhang, and Y. Wang, “Automatic retinal vessel segmentation using multi-scale superpixel chain tracking,” Digital Signal Processing 81, 26–42 (2018).
[Crossref]

Zhang, Z.

Y. Mei, M. Müller-Eschner, J. Yi, Z. Zhang, D. Chen, M. Kronlage, H. Tengg-Kobligk, H. U. Kauczor, D. Böckler, and S. Demirel, “Hemodynamics analyses in treated and untreated carotid arteries of the same patient: a preliminary study based on three patientcases,” Bio-Med. Mater. Eng. 26(s1), S299–S309 (2015).
[Crossref]

Zhao, J.

J. Zhao, J. Yang, D. Ai, H. Song, Y. Jiang, Y. Huang, L. Zhang, and Y. Wang, “Automatic retinal vessel segmentation using multi-scale superpixel chain tracking,” Digital Signal Processing 81, 26–42 (2018).
[Crossref]

Zheng, J.

D. Tang, C. Yang, J. Zheng, P. K. Woodard, J. E. Saffitz, J. D. Petruccelli, G. A. Sicard, and C. Yuan, “Local Maximal Stress Hypothesis and Computational Plaque Vulnerability Index for Atherosclerotic Plaque Assessment,” Ann. Biomed. Eng. 33(12), 1789–1801 (2005).
[Crossref]

Zhou, L.

Zhu, S.

Y. Huang, D. Tong, S. Zhu, L. Wu, Q. Mao, Z. Ibrahim, W. P. Lee, G. Brandacher, and J. U. Kang, “Evaluation of microvascular anastomosis using real-time, ultra-high-resolution, Fourier domain Doppler optical coherence tomography,” Plast. Reconstr. Surg. 135(4), 711e–720e (2015).
[Crossref]

Y. Huang, G. J. Furtmüller, D. Tong, S. Zhu, W. P. Lee, G. Brandacher, and J. U. Kang, “MEMS-based handheld fourier domain Doppler optical coherence tomography for intraoperative microvascular anastomosis imaging,” PLoS One 9(12), e114215 (2014).
[Crossref]

Y. Huang, Z. Ibrahim, D. Tong, S. Zhu, Q. Mao, J. Pang, W. P. Andree Lee, G. Brandacher, and J. U. Kang, “Microvascular anastomosis guidance and evaluation using real-time three-dimensional Fourier-domain Doppler optical coherence tomography,” J. Biomed. Opt. 18(11), 111404 (2013).
[Crossref]

Ann. Biomed. Eng. (1)

D. Tang, C. Yang, J. Zheng, P. K. Woodard, J. E. Saffitz, J. D. Petruccelli, G. A. Sicard, and C. Yuan, “Local Maximal Stress Hypothesis and Computational Plaque Vulnerability Index for Atherosclerotic Plaque Assessment,” Ann. Biomed. Eng. 33(12), 1789–1801 (2005).
[Crossref]

Appl. Opt. (1)

Bio-Med. Mater. Eng. (1)

Y. Mei, M. Müller-Eschner, J. Yi, Z. Zhang, D. Chen, M. Kronlage, H. Tengg-Kobligk, H. U. Kauczor, D. Böckler, and S. Demirel, “Hemodynamics analyses in treated and untreated carotid arteries of the same patient: a preliminary study based on three patientcases,” Bio-Med. Mater. Eng. 26(s1), S299–S309 (2015).
[Crossref]

Biomed. Opt. Express (7)

A. G. Roy, S. Conjeti, S. P. K. Karri, D. Sheet, A. Katouzian, C. Wachinger, and N. Navab, “ReLayNet: retinal layer and fluid segmentation of macular optical coherence tomography using fully convolutional networks,” Biomed. Opt. Express 8(8), 3627–3642 (2017).
[Crossref]

F. G. Venhuizen, B. van Ginneken, B. Liefers, F. van Asten, V. Schreur, S. Fauser, C. Hoyng, T. Theelen, and C. I. Sanchez, “Deep learning approach for the detection and quantification of intraretinal cystoid fluid in multivendor optical coherence tomography,” Biomed. Opt. Express 9(4), 1545–1569 (2018).
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F. Yi, I. Moon, and B. Javidi, “Automated red blood cells extraction from holographic images using fully convolutional neural networks,” Biomed. Opt. Express 8(10), 4466–4479 (2017).
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C. S. Lee, A. J. Tyring, N. P. Deruyter, Y. Wu, A. Rokem, and A. Y. Lee, “Deep-learning based automated segmentation of macular edema in optical coherence tomography,” Biomed. Opt. Express 8(7), 3440–3448 (2017).
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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(5), 2732–2744 (2017).
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A. Shah, L. Zhou, M. D. Abramoff, and X. Wu, “Multiple surface segmentation using convolution neural nets: application to retinal layer segmentation in OCT images,” Biomed. Opt. Express 9(9), 4509–4526 (2018).
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J. Hamwood, D. Alonso-Caneiro, S. A. Read, S. J. Vincent, and M. J. Collins, “Effect of patch size and network architecture on a convolutional neural network approach for automatic segmentation of OCT retinal layers,” Biomed. Opt. Express 9(7), 3049–3066 (2018).
[Crossref]

Digital Signal Processing (1)

J. Zhao, J. Yang, D. Ai, H. Song, Y. Jiang, Y. Huang, L. Zhang, and Y. Wang, “Automatic retinal vessel segmentation using multi-scale superpixel chain tracking,” Digital Signal Processing 81, 26–42 (2018).
[Crossref]

IEEE Access (1)

J. Fan, J. Yang, Y. Wang, S. Yang, D. Ai, Y. Huang, H. Song, A. Hao, and Y. Wang, “Multichannel Fully Convolutional Network for Coronary Artery Segmentation in X-ray Angiograms,” IEEE Access 6, 44635–44643 (2018).
[Crossref]

IEEE J Biomed. Health Inform. (2)

G. N. Girish, T. Bibhash, R. Sohini, R. Abhishek, and R. Jeny, “Segmentation of Intra-Retinal Cysts from Optical Coherence Tomography Images using a Fully Convolutional Neural Network Model,” IEEE J Biomed. Health Inform. 23(1), 296–304 (2019).
[Crossref]

A. Guha Roy, S. Conjeti, S. G. Carlier, P. K. Dutta, A. Kastrati, A. F. Laine, N. Navab, A. Katouzian, and D. Sheet, “Lumen Segmentation in Intravascular Optical Coherence Tomography Using Backscattering Tracked and Initialized Random Walks,” IEEE J Biomed. Health Inform. 20(2), 606–614 (2016).
[Crossref]

IEEE Trans. Med. Imag. (1)

Y. Lequan, H. Chen, Q. Dou, J. Qin, and P. A. Heng, “Automated melanoma recognition in dermoscopy images via very deep residual networks,” IEEE Trans. Med. Imag. 36(4), 994–1004 (2017).
[Crossref]

IEEE Trans. Med. Imaging (3)

L. Fang, S. Li, D. Cunefare, and S. Farsiu, “Segmentation Based Sparse Reconstruction of Optical Coherence Tomography Images,” IEEE Trans. Med. Imaging 36(2), 407–421 (2017).
[Crossref]

L. Fang, S. Li, R. P. McNabb, Q. Nie, A. N. Kuo, C. A. Toth, J. A. Izatt, and S. Farsiu, “Fast acquisition and reconstruction of optical coherence tomography images via sparse representation,” IEEE Trans. Med. Imaging 32(11), 2034–2049 (2013).
[Crossref]

A. Yazdanpanah, G. Hamar, B. R. Smith, and M. V. Sarunic, “Segmentation of intra-retinal layers from optical coherence tomgraphy images using an active contour approach,” IEEE Trans. Med. Imaging 30(2), 484–496 (2011).
[Crossref]

IEEE Trans. Pattern Anal. Mach. Intell. (1)

L. Grady, “Random walks for image segmentation,” IEEE Trans. Pattern Anal. Mach. Intell. 28(11), 1768–1783 (2006).
[Crossref]

J. Biomed. Opt. (1)

Y. Huang, Z. Ibrahim, D. Tong, S. Zhu, Q. Mao, J. Pang, W. P. Andree Lee, G. Brandacher, and J. U. Kang, “Microvascular anastomosis guidance and evaluation using real-time three-dimensional Fourier-domain Doppler optical coherence tomography,” J. Biomed. Opt. 18(11), 111404 (2013).
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L. Aida, “Alexis Carrel (1873–1944): Visionary vascular surgeon and pioneer in organ transplantation,” J. Med. Biogr. 22(3), 172–175 (2014).
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F. M. Leclère, M. Schoofs, F. Auger, B. Buys, and S. R. Mordon, “Blood Flow Assessment with Magnetic Resonance Imaging After 1.9 mm Diode Laser-Assisted Microvascular Anastomosis,” Lasers Surg. Med. 42(4), 299–305 (2010).
[Crossref]

Nat. Med. (1)

E. I. Chang, M. G. Galvez, J. P. Glotzbach, C. D. Hamou, S. El-fesi, C. T. Rappleye, K. M. Sommer, J. Rajadas, O. J. Abilez, G. G. Fuller, M. T. Longaker, and G. C. Gurtner, “Vascular anastomosis using controlled phase transitions in poloxamer gels,” Nat. Med. 17(9), 1147–1152 (2011).
[Crossref]

Opt. Express (1)

Opt. Lett. (1)

Plast. Reconstr. Surg. (1)

Y. Huang, D. Tong, S. Zhu, L. Wu, Q. Mao, Z. Ibrahim, W. P. Lee, G. Brandacher, and J. U. Kang, “Evaluation of microvascular anastomosis using real-time, ultra-high-resolution, Fourier domain Doppler optical coherence tomography,” Plast. Reconstr. Surg. 135(4), 711e–720e (2015).
[Crossref]

PLoS One (2)

Y. Huang, G. J. Furtmüller, D. Tong, S. Zhu, W. P. Lee, G. Brandacher, and J. U. Kang, “MEMS-based handheld fourier domain Doppler optical coherence tomography for intraoperative microvascular anastomosis imaging,” PLoS One 9(12), e114215 (2014).
[Crossref]

A. Liu, X. Yin, L. Shi, P. Li, K. L. Thornburg, R. K. Wang, and S. Rugonyi, “Biomechanics of the Chick Embryonic Heart Outflow Tract at HH18 Using 4D Optical Coherence TomographyImaging and Computational odeling,” PLoS One 7(7), e40869 (2012).
[Crossref]

Radiology (1)

S. A. Boppart, B. E. Bouma, C. Pitris, G. J. Tearny, and J. F. Southern, “Intraoperative assessment of microsurgery with three-dimensional optical coherence tomography,” Radiology 208(1), 81–86 (1998).
[Crossref]

Other (5)

K. Kamnitsas, L. Chen, C. Ledig, D. Rueckert, and B. Glocker, “Multiscale 3D convolutional neural networks for lesion segmentation in brain MRI,” in Proc of MICCAI Brain Lesion Workshop (2015).

O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional Networks for Biomedical Image S Microvascular egmentation,” arXiv: 1505.04597v1 [cs.CV] (2015).

P. F. Christ, M. E. A. Elshaer, F. Ettlinger, S. Tatavarty, M. Bickel, P. Bilic, M. Rempfler, M. Armbruster, F. Hofmann, M. D’Anastasi, W. H. Sommer, S. A. Ahmadi, and B. H. Menze, “Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields,” arXiv:1610.02177 [cs.CV] (2016).

J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition3431–3440 (2015).
[Crossref]

D. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint 1412.6980 (2014).

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Figures (11)

Fig. 1.
Fig. 1. Schematic of the first U-net framework of CU-net for intensity image segmentation
Fig. 2.
Fig. 2. Schematic of the second U-net framework of CU-net for phase image segmentation
Fig. 3.
Fig. 3. Flowchart of the U-net model training for intensity image segmentation.
Fig. 4.
Fig. 4. Flowchart of the U-net model training for phase image segmentation.
Fig. 5.
Fig. 5. Loss value vs. epoch for two U-net models in training process.
Fig. 6.
Fig. 6. Segmentation accuracy vs. epoch for two U-net models in training process.
Fig. 7.
Fig. 7. Segmentation results of four selective Doppler OCT image pairs using CU-net. (a-1)–(a-4): intensity image with outer boundary outlined by red curve; (b-1)–(b-4): phase image with outer and inner boundary outlined by red curve; (c-1)–(c-4): segmented binary masks. (Scale bar: 500 μm)
Fig. 8.
Fig. 8. 3D volume rendering of the segmented vessel from two different views: (a) front view (b) skewed front view (c) slice image of the P1 plane of the vessel (d) slice image of the P2 plane of the vessel and (e) slice image of the P3 plane of the vessel
Fig. 9.
Fig. 9. (a) the inner blood flowing lumen area and (b) the average inner blood flowing lumen area radius and its variation along the blood flow axis.
Fig. 10.
Fig. 10. Loss value (a) and accuracy (b) of the single U-net with the original phase images as architecture inputs in training process and the segmentation accuracy vs. epochs of the CU-net phase model (c) and single U-net phase model (d).
Fig. 11.
Fig. 11. Segmentation results comparison between CU-net phase model (left column) and single U-net phase model (middle column) and gold standard (right column) for two selective phase images: (a) single U-net phase model fails to detect a sharp thrombus intrusion (b) single U-net phase model forms an artifact of thrombus intrusion and creates obvious erroneous boundary. Black arrows point out the positions of segmentation artifact. (Scale bar: 250 μm)

Equations (1)

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S A ( S s e g , S g t ) = 2 | S s e g S g t | | S s e g | + | S g t |

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