Abstract

The aortic aneurysm is a disease originated mainly in the media layer of the aortic wall due to the occurrence of degraded areas of altered biological composition. These anomalous regions affect the structure and strength of the aorta artery, being their occurrence and extension proportional to the arterial vessel health. Optical Coherence Tomography (OCT) is applied to obtain cross-sectional images of the artery wall. The backscattering mechanisms in tissue make aorta images difficult to analyze due to noise and strong attenuation with penetration. The morphology of anomalies in pathological specimens is also diverse with amorphous shapes and varied dimensions, being these factors strongly related with tissue degradation and the aorta physiological condition. Hessian analysis of OCT images from aortic walls is used to assess the accurate delineation of these anomalous regions. A specific metric of the Hessian determinant is used to delineate degraded regions under blurry conditions and noise. A multiscale approach, based on an anisotropic Gaussian kernel filter, is applied to highlight and aggregate all the heterogeneity present in the aortic wall. An accuracy estimator metric has been implemented to evaluate and optimize the delineation process avoiding subjectivity. Finally, a degradation quantification score has been developed to assess aorta wall condition by OCT with validation against common histology.

© 2016 Optical Society of America

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  1. J. F. Matthias Bechtel, F. Noack, F. Sayk, A. W. Erasmi, C. Bartels, and H. H. Sievers, “Histopathological grading of ascending aortic aneurysm: comparison of patients with bicuspid versus tricuspid aortic valve,” J. Heart Valve Dis. 12(1), 54–61 (2003).
    [PubMed]
  2. E. Real, J. F. Val-Bernal, J. M. Revuelta, A. Pontón, M. C. Díez, M. Mayorga, J. M. López-Higuera, and O. M. Conde, “Identification of vessel wall degradation in ascending thoracic aortic aneurysms with OCT,” Biomed. Opt. Express 5(11), 4089–4100 (2014).
    [Crossref] [PubMed]
  3. A. F. Frangi, W. J. Niessen, R. M. Hoogeveen, T. van Walsum, and M. A. Viergever, “Model-based quantitation of 3-D magnetic resonance angiographic images,” IEEE Trans. Med. Imaging 18(10), 946–956 (1999).
    [Crossref] [PubMed]
  4. D. J. Kroon, “Hessian based Frangi Vesselness filter,” (2010) http://www.mathworks.com/matlabcentral/fileexchange/24409-hessian-based-frangi-vesselness-filter .
  5. J. Lee, J. Y. Jiang, W. Wu, F. Lesage, and D. A. Boas, “Statistical intensity variation analysis for rapid volumetric imaging of capillary network flux,” Biomed. Opt. Express 5(4), 1160–1172 (2014).
    [Crossref] [PubMed]
  6. Q. Li, S. Sone, and K. Doi, “Selective enhancement filters for nodules, vessels, and airway walls in two- and three-dimensional CT scans,” Med. Phys. 30(8), 2040–2051 (2003).
    [Crossref] [PubMed]
  7. J. Liu, J. M. White, and R. M. Summers, “Automated detection of blob structures by Hessian analysis and object scale,” IEEE 17th International Conference on Image Processing (ICIP), Hong Kong, 841–844 (2010).
    [Crossref]
  8. Y. Sato, S. Nakajima, N. Shiraga, H. Atsumi, S. Yoshida, T. Koller, G. Gerig, and R. Kikinis, “Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images,” Med. Image Anal. 2(2), 143–168 (1998).
    [Crossref] [PubMed]
  9. S. Yousefi, J. Qin, Z. Zhi, and R. K. Wang, “Label-free optical lymphangiography: Development of an automatic segmentation method applied to optical coherence tomography to visualize lymphatic vessels using Hessian filters,” J. Biomed. Opt. 18(8), 086004 (2013).
    [Crossref] [PubMed]
  10. S. Yousefi, T. Liu, and R. K. Wang, “Segmentation and quantification of blood vessels for OCT-based micro-angiograms using hybrid shape/intensity compounding,” Microvasc. Res. 97, 37–46 (2015).
    [Crossref] [PubMed]
  11. K. M. Poole, C. A. Patil, C. E. Nelson, D. R. McCormack, M. C. Madonna, C. L. Duvall, and M. C. Skala, “Longitudinal study of arteriogenesis with swept source optical coherence tomography and hyperspectral imaging,” Proc. SPIE 8934, 89341Z (2014).
    [Crossref]
  12. M. Kirby, A. M. D. Lee, T. Candido, C. Macaulay, P. Lane, S. Lam, and H. O. Coxson, “Automated segmentation of porcine airway wall layers using optical coherence tomography: Comparison with manual segmentation and histology,” Proc. SPIE 8927, 8927D (2014).
  13. E. Real, J. F. Val-Bernal, J. M. Revuelta, A. Pontón, M. Calvo Díez, M. Mayorga, J. M. López-Higuera, and O. M. Conde, “Enhanced delineation of degradation in aortic walls through OCT,” Proc. SPIE 9312, 931233 (2015).
    [Crossref]
  14. E. Real, J. F. Val-Bernal, A. Pontón, M. Calvo Díez, M. Mayorga, J. M. Revuelta, J. M. López-Higuera and O. M. Conde, “OCT for anomaly detection in aortic aneurysm resection,” IEEE Sensors Conference, Valencia, Spain, 2–5 Nov., pp. 694– 697 (2014).
  15. E. Real, A. Eguizábal, A. Pontón, M. C. Díez, J. Fernando Val-Bernal, M. Mayorga, J. M. Revuelta, J. M. López-Higuera, and O. M. Conde, “Optical coherence tomography assessment of vessel wall degradation in thoracic aortic aneurysms,” J. Biomed. Opt. 18(12), 126003 (2013).
    [Crossref] [PubMed]
  16. N. Otsu, “A Threshold Selection Method from Gray-Level Histograms,” IEEE Sys Man Cyb 9(1), 62–66 (1979).
    [Crossref]

2015 (2)

S. Yousefi, T. Liu, and R. K. Wang, “Segmentation and quantification of blood vessels for OCT-based micro-angiograms using hybrid shape/intensity compounding,” Microvasc. Res. 97, 37–46 (2015).
[Crossref] [PubMed]

E. Real, J. F. Val-Bernal, J. M. Revuelta, A. Pontón, M. Calvo Díez, M. Mayorga, J. M. López-Higuera, and O. M. Conde, “Enhanced delineation of degradation in aortic walls through OCT,” Proc. SPIE 9312, 931233 (2015).
[Crossref]

2014 (4)

K. M. Poole, C. A. Patil, C. E. Nelson, D. R. McCormack, M. C. Madonna, C. L. Duvall, and M. C. Skala, “Longitudinal study of arteriogenesis with swept source optical coherence tomography and hyperspectral imaging,” Proc. SPIE 8934, 89341Z (2014).
[Crossref]

M. Kirby, A. M. D. Lee, T. Candido, C. Macaulay, P. Lane, S. Lam, and H. O. Coxson, “Automated segmentation of porcine airway wall layers using optical coherence tomography: Comparison with manual segmentation and histology,” Proc. SPIE 8927, 8927D (2014).

E. Real, J. F. Val-Bernal, J. M. Revuelta, A. Pontón, M. C. Díez, M. Mayorga, J. M. López-Higuera, and O. M. Conde, “Identification of vessel wall degradation in ascending thoracic aortic aneurysms with OCT,” Biomed. Opt. Express 5(11), 4089–4100 (2014).
[Crossref] [PubMed]

J. Lee, J. Y. Jiang, W. Wu, F. Lesage, and D. A. Boas, “Statistical intensity variation analysis for rapid volumetric imaging of capillary network flux,” Biomed. Opt. Express 5(4), 1160–1172 (2014).
[Crossref] [PubMed]

2013 (2)

S. Yousefi, J. Qin, Z. Zhi, and R. K. Wang, “Label-free optical lymphangiography: Development of an automatic segmentation method applied to optical coherence tomography to visualize lymphatic vessels using Hessian filters,” J. Biomed. Opt. 18(8), 086004 (2013).
[Crossref] [PubMed]

E. Real, A. Eguizábal, A. Pontón, M. C. Díez, J. Fernando Val-Bernal, M. Mayorga, J. M. Revuelta, J. M. López-Higuera, and O. M. Conde, “Optical coherence tomography assessment of vessel wall degradation in thoracic aortic aneurysms,” J. Biomed. Opt. 18(12), 126003 (2013).
[Crossref] [PubMed]

2003 (2)

J. F. Matthias Bechtel, F. Noack, F. Sayk, A. W. Erasmi, C. Bartels, and H. H. Sievers, “Histopathological grading of ascending aortic aneurysm: comparison of patients with bicuspid versus tricuspid aortic valve,” J. Heart Valve Dis. 12(1), 54–61 (2003).
[PubMed]

Q. Li, S. Sone, and K. Doi, “Selective enhancement filters for nodules, vessels, and airway walls in two- and three-dimensional CT scans,” Med. Phys. 30(8), 2040–2051 (2003).
[Crossref] [PubMed]

1999 (1)

A. F. Frangi, W. J. Niessen, R. M. Hoogeveen, T. van Walsum, and M. A. Viergever, “Model-based quantitation of 3-D magnetic resonance angiographic images,” IEEE Trans. Med. Imaging 18(10), 946–956 (1999).
[Crossref] [PubMed]

1998 (1)

Y. Sato, S. Nakajima, N. Shiraga, H. Atsumi, S. Yoshida, T. Koller, G. Gerig, and R. Kikinis, “Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images,” Med. Image Anal. 2(2), 143–168 (1998).
[Crossref] [PubMed]

1979 (1)

N. Otsu, “A Threshold Selection Method from Gray-Level Histograms,” IEEE Sys Man Cyb 9(1), 62–66 (1979).
[Crossref]

Atsumi, H.

Y. Sato, S. Nakajima, N. Shiraga, H. Atsumi, S. Yoshida, T. Koller, G. Gerig, and R. Kikinis, “Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images,” Med. Image Anal. 2(2), 143–168 (1998).
[Crossref] [PubMed]

Bartels, C.

J. F. Matthias Bechtel, F. Noack, F. Sayk, A. W. Erasmi, C. Bartels, and H. H. Sievers, “Histopathological grading of ascending aortic aneurysm: comparison of patients with bicuspid versus tricuspid aortic valve,” J. Heart Valve Dis. 12(1), 54–61 (2003).
[PubMed]

Boas, D. A.

Calvo Díez, M.

E. Real, J. F. Val-Bernal, J. M. Revuelta, A. Pontón, M. Calvo Díez, M. Mayorga, J. M. López-Higuera, and O. M. Conde, “Enhanced delineation of degradation in aortic walls through OCT,” Proc. SPIE 9312, 931233 (2015).
[Crossref]

Candido, T.

M. Kirby, A. M. D. Lee, T. Candido, C. Macaulay, P. Lane, S. Lam, and H. O. Coxson, “Automated segmentation of porcine airway wall layers using optical coherence tomography: Comparison with manual segmentation and histology,” Proc. SPIE 8927, 8927D (2014).

Conde, O. M.

E. Real, J. F. Val-Bernal, J. M. Revuelta, A. Pontón, M. Calvo Díez, M. Mayorga, J. M. López-Higuera, and O. M. Conde, “Enhanced delineation of degradation in aortic walls through OCT,” Proc. SPIE 9312, 931233 (2015).
[Crossref]

E. Real, J. F. Val-Bernal, J. M. Revuelta, A. Pontón, M. C. Díez, M. Mayorga, J. M. López-Higuera, and O. M. Conde, “Identification of vessel wall degradation in ascending thoracic aortic aneurysms with OCT,” Biomed. Opt. Express 5(11), 4089–4100 (2014).
[Crossref] [PubMed]

E. Real, A. Eguizábal, A. Pontón, M. C. Díez, J. Fernando Val-Bernal, M. Mayorga, J. M. Revuelta, J. M. López-Higuera, and O. M. Conde, “Optical coherence tomography assessment of vessel wall degradation in thoracic aortic aneurysms,” J. Biomed. Opt. 18(12), 126003 (2013).
[Crossref] [PubMed]

Coxson, H. O.

M. Kirby, A. M. D. Lee, T. Candido, C. Macaulay, P. Lane, S. Lam, and H. O. Coxson, “Automated segmentation of porcine airway wall layers using optical coherence tomography: Comparison with manual segmentation and histology,” Proc. SPIE 8927, 8927D (2014).

Díez, M. C.

E. Real, J. F. Val-Bernal, J. M. Revuelta, A. Pontón, M. C. Díez, M. Mayorga, J. M. López-Higuera, and O. M. Conde, “Identification of vessel wall degradation in ascending thoracic aortic aneurysms with OCT,” Biomed. Opt. Express 5(11), 4089–4100 (2014).
[Crossref] [PubMed]

E. Real, A. Eguizábal, A. Pontón, M. C. Díez, J. Fernando Val-Bernal, M. Mayorga, J. M. Revuelta, J. M. López-Higuera, and O. M. Conde, “Optical coherence tomography assessment of vessel wall degradation in thoracic aortic aneurysms,” J. Biomed. Opt. 18(12), 126003 (2013).
[Crossref] [PubMed]

Doi, K.

Q. Li, S. Sone, and K. Doi, “Selective enhancement filters for nodules, vessels, and airway walls in two- and three-dimensional CT scans,” Med. Phys. 30(8), 2040–2051 (2003).
[Crossref] [PubMed]

Duvall, C. L.

K. M. Poole, C. A. Patil, C. E. Nelson, D. R. McCormack, M. C. Madonna, C. L. Duvall, and M. C. Skala, “Longitudinal study of arteriogenesis with swept source optical coherence tomography and hyperspectral imaging,” Proc. SPIE 8934, 89341Z (2014).
[Crossref]

Eguizábal, A.

E. Real, A. Eguizábal, A. Pontón, M. C. Díez, J. Fernando Val-Bernal, M. Mayorga, J. M. Revuelta, J. M. López-Higuera, and O. M. Conde, “Optical coherence tomography assessment of vessel wall degradation in thoracic aortic aneurysms,” J. Biomed. Opt. 18(12), 126003 (2013).
[Crossref] [PubMed]

Erasmi, A. W.

J. F. Matthias Bechtel, F. Noack, F. Sayk, A. W. Erasmi, C. Bartels, and H. H. Sievers, “Histopathological grading of ascending aortic aneurysm: comparison of patients with bicuspid versus tricuspid aortic valve,” J. Heart Valve Dis. 12(1), 54–61 (2003).
[PubMed]

Fernando Val-Bernal, J.

E. Real, A. Eguizábal, A. Pontón, M. C. Díez, J. Fernando Val-Bernal, M. Mayorga, J. M. Revuelta, J. M. López-Higuera, and O. M. Conde, “Optical coherence tomography assessment of vessel wall degradation in thoracic aortic aneurysms,” J. Biomed. Opt. 18(12), 126003 (2013).
[Crossref] [PubMed]

Frangi, A. F.

A. F. Frangi, W. J. Niessen, R. M. Hoogeveen, T. van Walsum, and M. A. Viergever, “Model-based quantitation of 3-D magnetic resonance angiographic images,” IEEE Trans. Med. Imaging 18(10), 946–956 (1999).
[Crossref] [PubMed]

Gerig, G.

Y. Sato, S. Nakajima, N. Shiraga, H. Atsumi, S. Yoshida, T. Koller, G. Gerig, and R. Kikinis, “Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images,” Med. Image Anal. 2(2), 143–168 (1998).
[Crossref] [PubMed]

Hoogeveen, R. M.

A. F. Frangi, W. J. Niessen, R. M. Hoogeveen, T. van Walsum, and M. A. Viergever, “Model-based quantitation of 3-D magnetic resonance angiographic images,” IEEE Trans. Med. Imaging 18(10), 946–956 (1999).
[Crossref] [PubMed]

Jiang, J. Y.

Kikinis, R.

Y. Sato, S. Nakajima, N. Shiraga, H. Atsumi, S. Yoshida, T. Koller, G. Gerig, and R. Kikinis, “Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images,” Med. Image Anal. 2(2), 143–168 (1998).
[Crossref] [PubMed]

Kirby, M.

M. Kirby, A. M. D. Lee, T. Candido, C. Macaulay, P. Lane, S. Lam, and H. O. Coxson, “Automated segmentation of porcine airway wall layers using optical coherence tomography: Comparison with manual segmentation and histology,” Proc. SPIE 8927, 8927D (2014).

Koller, T.

Y. Sato, S. Nakajima, N. Shiraga, H. Atsumi, S. Yoshida, T. Koller, G. Gerig, and R. Kikinis, “Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images,” Med. Image Anal. 2(2), 143–168 (1998).
[Crossref] [PubMed]

Lam, S.

M. Kirby, A. M. D. Lee, T. Candido, C. Macaulay, P. Lane, S. Lam, and H. O. Coxson, “Automated segmentation of porcine airway wall layers using optical coherence tomography: Comparison with manual segmentation and histology,” Proc. SPIE 8927, 8927D (2014).

Lane, P.

M. Kirby, A. M. D. Lee, T. Candido, C. Macaulay, P. Lane, S. Lam, and H. O. Coxson, “Automated segmentation of porcine airway wall layers using optical coherence tomography: Comparison with manual segmentation and histology,” Proc. SPIE 8927, 8927D (2014).

Lee, A. M. D.

M. Kirby, A. M. D. Lee, T. Candido, C. Macaulay, P. Lane, S. Lam, and H. O. Coxson, “Automated segmentation of porcine airway wall layers using optical coherence tomography: Comparison with manual segmentation and histology,” Proc. SPIE 8927, 8927D (2014).

Lee, J.

Lesage, F.

Li, Q.

Q. Li, S. Sone, and K. Doi, “Selective enhancement filters for nodules, vessels, and airway walls in two- and three-dimensional CT scans,” Med. Phys. 30(8), 2040–2051 (2003).
[Crossref] [PubMed]

Liu, T.

S. Yousefi, T. Liu, and R. K. Wang, “Segmentation and quantification of blood vessels for OCT-based micro-angiograms using hybrid shape/intensity compounding,” Microvasc. Res. 97, 37–46 (2015).
[Crossref] [PubMed]

López-Higuera, J. M.

E. Real, J. F. Val-Bernal, J. M. Revuelta, A. Pontón, M. Calvo Díez, M. Mayorga, J. M. López-Higuera, and O. M. Conde, “Enhanced delineation of degradation in aortic walls through OCT,” Proc. SPIE 9312, 931233 (2015).
[Crossref]

E. Real, J. F. Val-Bernal, J. M. Revuelta, A. Pontón, M. C. Díez, M. Mayorga, J. M. López-Higuera, and O. M. Conde, “Identification of vessel wall degradation in ascending thoracic aortic aneurysms with OCT,” Biomed. Opt. Express 5(11), 4089–4100 (2014).
[Crossref] [PubMed]

E. Real, A. Eguizábal, A. Pontón, M. C. Díez, J. Fernando Val-Bernal, M. Mayorga, J. M. Revuelta, J. M. López-Higuera, and O. M. Conde, “Optical coherence tomography assessment of vessel wall degradation in thoracic aortic aneurysms,” J. Biomed. Opt. 18(12), 126003 (2013).
[Crossref] [PubMed]

Macaulay, C.

M. Kirby, A. M. D. Lee, T. Candido, C. Macaulay, P. Lane, S. Lam, and H. O. Coxson, “Automated segmentation of porcine airway wall layers using optical coherence tomography: Comparison with manual segmentation and histology,” Proc. SPIE 8927, 8927D (2014).

Madonna, M. C.

K. M. Poole, C. A. Patil, C. E. Nelson, D. R. McCormack, M. C. Madonna, C. L. Duvall, and M. C. Skala, “Longitudinal study of arteriogenesis with swept source optical coherence tomography and hyperspectral imaging,” Proc. SPIE 8934, 89341Z (2014).
[Crossref]

Matthias Bechtel, J. F.

J. F. Matthias Bechtel, F. Noack, F. Sayk, A. W. Erasmi, C. Bartels, and H. H. Sievers, “Histopathological grading of ascending aortic aneurysm: comparison of patients with bicuspid versus tricuspid aortic valve,” J. Heart Valve Dis. 12(1), 54–61 (2003).
[PubMed]

Mayorga, M.

E. Real, J. F. Val-Bernal, J. M. Revuelta, A. Pontón, M. Calvo Díez, M. Mayorga, J. M. López-Higuera, and O. M. Conde, “Enhanced delineation of degradation in aortic walls through OCT,” Proc. SPIE 9312, 931233 (2015).
[Crossref]

E. Real, J. F. Val-Bernal, J. M. Revuelta, A. Pontón, M. C. Díez, M. Mayorga, J. M. López-Higuera, and O. M. Conde, “Identification of vessel wall degradation in ascending thoracic aortic aneurysms with OCT,” Biomed. Opt. Express 5(11), 4089–4100 (2014).
[Crossref] [PubMed]

E. Real, A. Eguizábal, A. Pontón, M. C. Díez, J. Fernando Val-Bernal, M. Mayorga, J. M. Revuelta, J. M. López-Higuera, and O. M. Conde, “Optical coherence tomography assessment of vessel wall degradation in thoracic aortic aneurysms,” J. Biomed. Opt. 18(12), 126003 (2013).
[Crossref] [PubMed]

McCormack, D. R.

K. M. Poole, C. A. Patil, C. E. Nelson, D. R. McCormack, M. C. Madonna, C. L. Duvall, and M. C. Skala, “Longitudinal study of arteriogenesis with swept source optical coherence tomography and hyperspectral imaging,” Proc. SPIE 8934, 89341Z (2014).
[Crossref]

Nakajima, S.

Y. Sato, S. Nakajima, N. Shiraga, H. Atsumi, S. Yoshida, T. Koller, G. Gerig, and R. Kikinis, “Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images,” Med. Image Anal. 2(2), 143–168 (1998).
[Crossref] [PubMed]

Nelson, C. E.

K. M. Poole, C. A. Patil, C. E. Nelson, D. R. McCormack, M. C. Madonna, C. L. Duvall, and M. C. Skala, “Longitudinal study of arteriogenesis with swept source optical coherence tomography and hyperspectral imaging,” Proc. SPIE 8934, 89341Z (2014).
[Crossref]

Niessen, W. J.

A. F. Frangi, W. J. Niessen, R. M. Hoogeveen, T. van Walsum, and M. A. Viergever, “Model-based quantitation of 3-D magnetic resonance angiographic images,” IEEE Trans. Med. Imaging 18(10), 946–956 (1999).
[Crossref] [PubMed]

Noack, F.

J. F. Matthias Bechtel, F. Noack, F. Sayk, A. W. Erasmi, C. Bartels, and H. H. Sievers, “Histopathological grading of ascending aortic aneurysm: comparison of patients with bicuspid versus tricuspid aortic valve,” J. Heart Valve Dis. 12(1), 54–61 (2003).
[PubMed]

Otsu, N.

N. Otsu, “A Threshold Selection Method from Gray-Level Histograms,” IEEE Sys Man Cyb 9(1), 62–66 (1979).
[Crossref]

Patil, C. A.

K. M. Poole, C. A. Patil, C. E. Nelson, D. R. McCormack, M. C. Madonna, C. L. Duvall, and M. C. Skala, “Longitudinal study of arteriogenesis with swept source optical coherence tomography and hyperspectral imaging,” Proc. SPIE 8934, 89341Z (2014).
[Crossref]

Pontón, A.

E. Real, J. F. Val-Bernal, J. M. Revuelta, A. Pontón, M. Calvo Díez, M. Mayorga, J. M. López-Higuera, and O. M. Conde, “Enhanced delineation of degradation in aortic walls through OCT,” Proc. SPIE 9312, 931233 (2015).
[Crossref]

E. Real, J. F. Val-Bernal, J. M. Revuelta, A. Pontón, M. C. Díez, M. Mayorga, J. M. López-Higuera, and O. M. Conde, “Identification of vessel wall degradation in ascending thoracic aortic aneurysms with OCT,” Biomed. Opt. Express 5(11), 4089–4100 (2014).
[Crossref] [PubMed]

E. Real, A. Eguizábal, A. Pontón, M. C. Díez, J. Fernando Val-Bernal, M. Mayorga, J. M. Revuelta, J. M. López-Higuera, and O. M. Conde, “Optical coherence tomography assessment of vessel wall degradation in thoracic aortic aneurysms,” J. Biomed. Opt. 18(12), 126003 (2013).
[Crossref] [PubMed]

Poole, K. M.

K. M. Poole, C. A. Patil, C. E. Nelson, D. R. McCormack, M. C. Madonna, C. L. Duvall, and M. C. Skala, “Longitudinal study of arteriogenesis with swept source optical coherence tomography and hyperspectral imaging,” Proc. SPIE 8934, 89341Z (2014).
[Crossref]

Qin, J.

S. Yousefi, J. Qin, Z. Zhi, and R. K. Wang, “Label-free optical lymphangiography: Development of an automatic segmentation method applied to optical coherence tomography to visualize lymphatic vessels using Hessian filters,” J. Biomed. Opt. 18(8), 086004 (2013).
[Crossref] [PubMed]

Real, E.

E. Real, J. F. Val-Bernal, J. M. Revuelta, A. Pontón, M. Calvo Díez, M. Mayorga, J. M. López-Higuera, and O. M. Conde, “Enhanced delineation of degradation in aortic walls through OCT,” Proc. SPIE 9312, 931233 (2015).
[Crossref]

E. Real, J. F. Val-Bernal, J. M. Revuelta, A. Pontón, M. C. Díez, M. Mayorga, J. M. López-Higuera, and O. M. Conde, “Identification of vessel wall degradation in ascending thoracic aortic aneurysms with OCT,” Biomed. Opt. Express 5(11), 4089–4100 (2014).
[Crossref] [PubMed]

E. Real, A. Eguizábal, A. Pontón, M. C. Díez, J. Fernando Val-Bernal, M. Mayorga, J. M. Revuelta, J. M. López-Higuera, and O. M. Conde, “Optical coherence tomography assessment of vessel wall degradation in thoracic aortic aneurysms,” J. Biomed. Opt. 18(12), 126003 (2013).
[Crossref] [PubMed]

Revuelta, J. M.

E. Real, J. F. Val-Bernal, J. M. Revuelta, A. Pontón, M. Calvo Díez, M. Mayorga, J. M. López-Higuera, and O. M. Conde, “Enhanced delineation of degradation in aortic walls through OCT,” Proc. SPIE 9312, 931233 (2015).
[Crossref]

E. Real, J. F. Val-Bernal, J. M. Revuelta, A. Pontón, M. C. Díez, M. Mayorga, J. M. López-Higuera, and O. M. Conde, “Identification of vessel wall degradation in ascending thoracic aortic aneurysms with OCT,” Biomed. Opt. Express 5(11), 4089–4100 (2014).
[Crossref] [PubMed]

E. Real, A. Eguizábal, A. Pontón, M. C. Díez, J. Fernando Val-Bernal, M. Mayorga, J. M. Revuelta, J. M. López-Higuera, and O. M. Conde, “Optical coherence tomography assessment of vessel wall degradation in thoracic aortic aneurysms,” J. Biomed. Opt. 18(12), 126003 (2013).
[Crossref] [PubMed]

Sato, Y.

Y. Sato, S. Nakajima, N. Shiraga, H. Atsumi, S. Yoshida, T. Koller, G. Gerig, and R. Kikinis, “Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images,” Med. Image Anal. 2(2), 143–168 (1998).
[Crossref] [PubMed]

Sayk, F.

J. F. Matthias Bechtel, F. Noack, F. Sayk, A. W. Erasmi, C. Bartels, and H. H. Sievers, “Histopathological grading of ascending aortic aneurysm: comparison of patients with bicuspid versus tricuspid aortic valve,” J. Heart Valve Dis. 12(1), 54–61 (2003).
[PubMed]

Shiraga, N.

Y. Sato, S. Nakajima, N. Shiraga, H. Atsumi, S. Yoshida, T. Koller, G. Gerig, and R. Kikinis, “Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images,” Med. Image Anal. 2(2), 143–168 (1998).
[Crossref] [PubMed]

Sievers, H. H.

J. F. Matthias Bechtel, F. Noack, F. Sayk, A. W. Erasmi, C. Bartels, and H. H. Sievers, “Histopathological grading of ascending aortic aneurysm: comparison of patients with bicuspid versus tricuspid aortic valve,” J. Heart Valve Dis. 12(1), 54–61 (2003).
[PubMed]

Skala, M. C.

K. M. Poole, C. A. Patil, C. E. Nelson, D. R. McCormack, M. C. Madonna, C. L. Duvall, and M. C. Skala, “Longitudinal study of arteriogenesis with swept source optical coherence tomography and hyperspectral imaging,” Proc. SPIE 8934, 89341Z (2014).
[Crossref]

Sone, S.

Q. Li, S. Sone, and K. Doi, “Selective enhancement filters for nodules, vessels, and airway walls in two- and three-dimensional CT scans,” Med. Phys. 30(8), 2040–2051 (2003).
[Crossref] [PubMed]

Val-Bernal, J. F.

E. Real, J. F. Val-Bernal, J. M. Revuelta, A. Pontón, M. Calvo Díez, M. Mayorga, J. M. López-Higuera, and O. M. Conde, “Enhanced delineation of degradation in aortic walls through OCT,” Proc. SPIE 9312, 931233 (2015).
[Crossref]

E. Real, J. F. Val-Bernal, J. M. Revuelta, A. Pontón, M. C. Díez, M. Mayorga, J. M. López-Higuera, and O. M. Conde, “Identification of vessel wall degradation in ascending thoracic aortic aneurysms with OCT,” Biomed. Opt. Express 5(11), 4089–4100 (2014).
[Crossref] [PubMed]

van Walsum, T.

A. F. Frangi, W. J. Niessen, R. M. Hoogeveen, T. van Walsum, and M. A. Viergever, “Model-based quantitation of 3-D magnetic resonance angiographic images,” IEEE Trans. Med. Imaging 18(10), 946–956 (1999).
[Crossref] [PubMed]

Viergever, M. A.

A. F. Frangi, W. J. Niessen, R. M. Hoogeveen, T. van Walsum, and M. A. Viergever, “Model-based quantitation of 3-D magnetic resonance angiographic images,” IEEE Trans. Med. Imaging 18(10), 946–956 (1999).
[Crossref] [PubMed]

Wang, R. K.

S. Yousefi, T. Liu, and R. K. Wang, “Segmentation and quantification of blood vessels for OCT-based micro-angiograms using hybrid shape/intensity compounding,” Microvasc. Res. 97, 37–46 (2015).
[Crossref] [PubMed]

S. Yousefi, J. Qin, Z. Zhi, and R. K. Wang, “Label-free optical lymphangiography: Development of an automatic segmentation method applied to optical coherence tomography to visualize lymphatic vessels using Hessian filters,” J. Biomed. Opt. 18(8), 086004 (2013).
[Crossref] [PubMed]

Wu, W.

Yoshida, S.

Y. Sato, S. Nakajima, N. Shiraga, H. Atsumi, S. Yoshida, T. Koller, G. Gerig, and R. Kikinis, “Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images,” Med. Image Anal. 2(2), 143–168 (1998).
[Crossref] [PubMed]

Yousefi, S.

S. Yousefi, T. Liu, and R. K. Wang, “Segmentation and quantification of blood vessels for OCT-based micro-angiograms using hybrid shape/intensity compounding,” Microvasc. Res. 97, 37–46 (2015).
[Crossref] [PubMed]

S. Yousefi, J. Qin, Z. Zhi, and R. K. Wang, “Label-free optical lymphangiography: Development of an automatic segmentation method applied to optical coherence tomography to visualize lymphatic vessels using Hessian filters,” J. Biomed. Opt. 18(8), 086004 (2013).
[Crossref] [PubMed]

Zhi, Z.

S. Yousefi, J. Qin, Z. Zhi, and R. K. Wang, “Label-free optical lymphangiography: Development of an automatic segmentation method applied to optical coherence tomography to visualize lymphatic vessels using Hessian filters,” J. Biomed. Opt. 18(8), 086004 (2013).
[Crossref] [PubMed]

Biomed. Opt. Express (2)

IEEE Sys Man Cyb (1)

N. Otsu, “A Threshold Selection Method from Gray-Level Histograms,” IEEE Sys Man Cyb 9(1), 62–66 (1979).
[Crossref]

IEEE Trans. Med. Imaging (1)

A. F. Frangi, W. J. Niessen, R. M. Hoogeveen, T. van Walsum, and M. A. Viergever, “Model-based quantitation of 3-D magnetic resonance angiographic images,” IEEE Trans. Med. Imaging 18(10), 946–956 (1999).
[Crossref] [PubMed]

J. Biomed. Opt. (2)

S. Yousefi, J. Qin, Z. Zhi, and R. K. Wang, “Label-free optical lymphangiography: Development of an automatic segmentation method applied to optical coherence tomography to visualize lymphatic vessels using Hessian filters,” J. Biomed. Opt. 18(8), 086004 (2013).
[Crossref] [PubMed]

E. Real, A. Eguizábal, A. Pontón, M. C. Díez, J. Fernando Val-Bernal, M. Mayorga, J. M. Revuelta, J. M. López-Higuera, and O. M. Conde, “Optical coherence tomography assessment of vessel wall degradation in thoracic aortic aneurysms,” J. Biomed. Opt. 18(12), 126003 (2013).
[Crossref] [PubMed]

J. Heart Valve Dis. (1)

J. F. Matthias Bechtel, F. Noack, F. Sayk, A. W. Erasmi, C. Bartels, and H. H. Sievers, “Histopathological grading of ascending aortic aneurysm: comparison of patients with bicuspid versus tricuspid aortic valve,” J. Heart Valve Dis. 12(1), 54–61 (2003).
[PubMed]

Med. Image Anal. (1)

Y. Sato, S. Nakajima, N. Shiraga, H. Atsumi, S. Yoshida, T. Koller, G. Gerig, and R. Kikinis, “Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images,” Med. Image Anal. 2(2), 143–168 (1998).
[Crossref] [PubMed]

Med. Phys. (1)

Q. Li, S. Sone, and K. Doi, “Selective enhancement filters for nodules, vessels, and airway walls in two- and three-dimensional CT scans,” Med. Phys. 30(8), 2040–2051 (2003).
[Crossref] [PubMed]

Microvasc. Res. (1)

S. Yousefi, T. Liu, and R. K. Wang, “Segmentation and quantification of blood vessels for OCT-based micro-angiograms using hybrid shape/intensity compounding,” Microvasc. Res. 97, 37–46 (2015).
[Crossref] [PubMed]

Proc. SPIE (3)

K. M. Poole, C. A. Patil, C. E. Nelson, D. R. McCormack, M. C. Madonna, C. L. Duvall, and M. C. Skala, “Longitudinal study of arteriogenesis with swept source optical coherence tomography and hyperspectral imaging,” Proc. SPIE 8934, 89341Z (2014).
[Crossref]

M. Kirby, A. M. D. Lee, T. Candido, C. Macaulay, P. Lane, S. Lam, and H. O. Coxson, “Automated segmentation of porcine airway wall layers using optical coherence tomography: Comparison with manual segmentation and histology,” Proc. SPIE 8927, 8927D (2014).

E. Real, J. F. Val-Bernal, J. M. Revuelta, A. Pontón, M. Calvo Díez, M. Mayorga, J. M. López-Higuera, and O. M. Conde, “Enhanced delineation of degradation in aortic walls through OCT,” Proc. SPIE 9312, 931233 (2015).
[Crossref]

Other (3)

E. Real, J. F. Val-Bernal, A. Pontón, M. Calvo Díez, M. Mayorga, J. M. Revuelta, J. M. López-Higuera and O. M. Conde, “OCT for anomaly detection in aortic aneurysm resection,” IEEE Sensors Conference, Valencia, Spain, 2–5 Nov., pp. 694– 697 (2014).

J. Liu, J. M. White, and R. M. Summers, “Automated detection of blob structures by Hessian analysis and object scale,” IEEE 17th International Conference on Image Processing (ICIP), Hong Kong, 841–844 (2010).
[Crossref]

D. J. Kroon, “Hessian based Frangi Vesselness filter,” (2010) http://www.mathworks.com/matlabcentral/fileexchange/24409-hessian-based-frangi-vesselness-filter .

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Figures (12)

Fig. 1
Fig. 1 Global procedure for the delimitation of anomalous regions in OCT images. The final purpose of delimitation is the quantification of degradation in the OCT B-scans.
Fig. 2
Fig. 2 Second order partial derivatives of the Gaussian kernel for different σx and σy values.
Fig. 3
Fig. 3 Simulated anomaly (a), detected anomaly with border in green (b) and area enclosing the detected anomaly (c). The size of the surrounding area is defined by a width σx. The mean grey level intensity (8 bits, 0 to 255 levels) is 218.35 in (a), 91.2 in the delimited section in (b) and 130.0 in the delimited section in (c).
Fig. 4
Fig. 4 OCT image from an aneurismatic aorta (a) and zoom for illustration purposes (b).
Fig. 5
Fig. 5 Individual Aix, σy) obtained for different σx and σy parameters. Pseudo-color represents normalized Aix, σy) from its minimum value (blue) to the maximum (red) with 255 colors representation. Pink lines represent the air-tissue interface computed in the pre-processing stage. Application of Otsu’s threshold method to these Aix, σy) provides the anomaly delineation (black lines).
Fig. 6
Fig. 6 Analysis of the delineation dependence on the kernel size, σx, maintaining an isotropic kernel with ratio = 1. Green lines represent the algorithm delineation output. Blue shaded regions represent manual reference delineation.
Fig. 7
Fig. 7 Analysis of the delineation dependence on the kernel size orientation maintaining σx = 7 always fixed and varying σy as a function of the ratio value. Green lines represent the algorithm delineation output. Blue shaded regions represent manual reference delineation.
Fig. 8
Fig. 8 Comparison of the best single application of the algorithm (top, σx = 7, ratio = 1) and the best multiscale aggregate according to Δ (bottom, σx = 3,5,7 and for each of them ratio = 1). On the first column (a, d), the original image with anomalies remarked in green and enclosing area in blue. The second row (b, e), present the segmentation of the anomalies with its mean intensity (I). The third column (c, f) represents the regions that surround the anomalies, required for the estimation of the accuracy metric, and its mean intensity (I).
Fig. 9
Fig. 9 Comparison of the original image (a) with different anomaly delineation tests. In (b), the manual delineation is shown. In (c), the result of the Ao aggregate is displayed. The individual Aix, σy) forming part of the final Ao are displayed from (d) to (f).
Fig. 10
Fig. 10 Multiscale delineation result for a severely degraded sample (top) and lowly degraded sample (bottom). Histology images (Elastic fibers-Verhoeff’s Van Gieson stain) are also processed (a, c) for comparison with OCT images of the same samples (b, d respectively). Note that dimensions and resolution are different in the case of OCT and histology. Although images are obtained in the same sample position, perfect correlation is not possible due to histology handling and cutting procedure.
Fig. 11
Fig. 11 OCT degradation score calculated on a B-scan basis. The boxplot representation includes the variation of all B-scans estimated from the same specimen. Red line: median of all the B-scans of each specimen (50% percentile). Blue box: 25 to 75% percentile. Black line: approx. 99.3% percentile. Red dots: outliers.
Fig. 12
Fig. 12 Histological degradation quantification score (a) and OCT degradation quantification score (b).

Tables (1)

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Table 1 Accuracy metric Δ, computed for different σx and ratio values

Equations (11)

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H f(x,y) =[ 2 f(x,y) x 2 2 f(x,y) xy 2 f(x,y) yx 2 f(x,y) y 2 ]
( f(x,y)k(x,y) ) x = f(x,y) x k(x,y)=f(x,y)* k(x,y) x
G (x,y) = 1 2π σ x σ y e ( ( x μ x ) 2 2 σ x 2 + ( y μ y ) 2 2 σ y 2 )
{ 2 G (x,y) x 2 Gxx=( ( 2x2 μ x ) 2 8π σ x 5 σ y 1 2π σ x 3 σ y ) e ( ( x μ x ) 2 2 σ x 2 + ( y μ y ) 2 2 σ y 2 ) 2 G (x,y) y 2 Gyy=( ( 2y2 μ y ) 2 8π σ x σ y 5 1 2π σ x σ y 3 ) e ( ( x μ x ) 2 2 σ x 2 + ( y μ y ) 2 2 σ y 2 ) 2 G (x,y) xy Gxy= ( 2y2 μ y )( 2y2 μ y ) 8π σ y 3 σ y 3 e ( ( x μ x ) 2 2 σ x 2 + ( y μ y ) 2 2 σ y 2 ) 2 G (x,y) yx Gyx= 2 G (x,y) xy
Hv=λv
A i ( σ x , σ y )={ 0 λ 1 <0 λ 1 max( λ 1 ) λ 1 0
Δ=| I ¯ Anomalies - I ¯ Enclosing |
ratio= σ y σ x
A o =max( A i ( σ x , σ y ))
B-scan score= k=1 Na e a k 512*1024
OCT score= 1 Nb Nb B-scan score

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