Abstract

Diabetes is characterized by hyperglycemia that can result from the loss of pancreatic insulin secreting β-cells in the islets of Langerhans. We analyzed ex vivo the entire gastric and duodenal lobes of a murine pancreas using extended-focus Optical Coherence Microscopy (xfOCM). To identify and quantify the islets of Langerhans observed in xfOCM tomograms we implemented an active contour algorithm based on the level set method. We show that xfOCM reveals a three-dimensional islet distribution consistent with Optical Projection Tomography, albeit with a higher resolution that also enables the detection of the smallest islets (≤ 8000 μm3). Although this category of the smallest islets represents only a negligible volume compared to the total β-cell volume, a recent study suggests that these islets, located at the periphery, are the first to be destroyed when type I diabetes develops. Our results underline the capability of xfOCM to contribute to the understanding of the development of diabetes, especially when considering islet volume distribution instead of the total β-cell volume only.

© 2012 OSA

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2011 (8)

I. Ghorbel, F. Rossant, I. Bloch, S. Tick, and M. Paques, “Automated segmentation of macular layers in OCT images and quantitative evaluation of performances,” Pattern Recogn.44, 1590–1603 (2011).
[CrossRef]

A. Hörnblad, A. Cheddad, and U. Ahlgren, “An improved protocol for optical projection tomography imaging reveals lobular heterogeneities in pancreatic islet and β-cell mass distribution,” Islets3, 1–5 (2011).
[CrossRef]

E. M. Akirav, M.-T. Baquero, L. W. Opare-Addo, M. Akirav, E. Galvan, J. A. Kushner, D. L. Rimm, and K. C. Herold, “Glucose and inflammation control islet vascular density and β-cell function in NOD mice: control of islet vasculature and vascular endothelial growth factor by glucose,” Diabetes60, 876–883 (2011).
[CrossRef] [PubMed]

P.-O. Bastien-Dionne, L. Valenti, N. Kon, W. Gu, and J. Buteau, “Glucagon-like peptide 1 inhibits the sirtuin deacetylase SirT1 to stimulate pancreatic β-cell mass expansion,” Diabetes60, 3217–3222 (2011).
[CrossRef] [PubMed]

M. Riopel, M. Krishnamurthy, J. Li, S. Liu, A. Leask, and R. Wang, “Conditional β1-integrin-deficient mice display impaired pancreatic β cell function,” J. Pathol.224, 45–55 (2011).
[CrossRef] [PubMed]

D. Choi, E. P. Cai, S. A. Schroer, L. Wang, and M. Woo, “Vhl is required for normal pancreatic β cell function and the maintenance of β cell mass with age in mice,” Lab. Invest.91, 527–538 (2011).
[CrossRef] [PubMed]

American Diabetes Association“Diagnosis and classification of diabetes mellitus,” Diabetes Care34, S62–S69 (2011).
[PubMed]

N. Iftimia, S. Cizginer, V. Deshpande, M. Pitman, S. Tatli, N. A. Iftimia, D. X. Hammer, M. Mujat, T. Ustun, R. D. Ferguson, and W. R. Brugge, “Differentiation of pancreatic cysts with optical coherence tomography (OCT) imaging: an ex vivo pilot study,” Biomed. Opt. Express2, 2372–2382 (2011).
[CrossRef] [PubMed]

2010 (10)

S. J. Chiu, X. T. Li, P. Nicholas, C. A. Toth, J. A. Izatt, and S. Farsiu, “Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation,” Opt. Express18, 19413–19428 (2010).
[CrossRef] [PubMed]

J. A. Bluestone, K. Herold, and G. Eisenbarth, “Genetics, pathogenesis and clinical interventions in type 1 diabetes,” Nature464, 1293–1300 (2010).
[CrossRef] [PubMed]

Y. Lin and S. Zhongjie, “Current views on type 2 diabetes,” J. Endocrinol.204, 1–11 (2010).
[CrossRef]

D. Wild, A. Wicki, R. Mansi, M. Béhé, B. Keil, P. Bernhardt, G. Christofori, P. J. Ell, and H. R. Mäcke, “Exendin-4-based radiopharmaceuticals for glucagonlike peptide-1 receptor PET/CT and SPECT/CT,” J. Nucl. Med.51, 1059–1067 (2010).
[CrossRef] [PubMed]

M. Brom, K. Andralojć, W. J. G. Oyen, O. C. Boerman, and M. Gotthardt, “Development of radiotracers for the determination of the beta-cell mass in vivo,” Curr. Pharm. Design16, 1561–1567 (2010).
[CrossRef]

L. R. Nyman, E. Ford, A. C. Powers, and D. W. Piston, “Glucose-dependent blood flow dynamics in murine pancreatic islets in vivo,” Am. J. Physiol.-Endoc. M.298, E807–E814 (2010).

M. Villiger, J. Goulley, E. J. Martin-Williams, A. Grapin-Botton, and T. Lasser, “Towards high resolution optical imaging of beta cells in vivo,” Curr. Pharm. Design16, 1595–1608 (2010).
[CrossRef]

M. Chintinne, G. Stangé, B. Denys, P. In ’t Veld, K. Hellemans, M. Pipeleers-Marichal, Z. Ling, and D. Pipeleers, “Contribution of postnatally formed small beta cell aggregates to functional beta cell mass in adult rat pancreas,” Diabetologia53, 2380–2388 (2010).
[CrossRef] [PubMed]

D. Bosco, M. Armanet, P. Morel, N. Niclauss, A. Sgroi, Y. D. Muller, L. Giovannoni, and T. Berney, “Unique Arrangement of α- and β-cells in Human Islets of Langerhans,” Diabetes59, 1202–1210 (2010).
[CrossRef] [PubMed]

T. Alanentalo, A. Hörnblad, S. Mayans, A. K. Nilsson, J. Sharpe, A. Larefalk, U. Ahlgren, and D. Holmberg, “Quantification and three-dimensional imaging of the insulitis-induced destruction of β-cells in murine type 1 diabetes,” Diabetes59, 1756–1764 (2010).
[CrossRef] [PubMed]

2009 (4)

A. Clauset, C. R. Shalizi, and M. E. J. Newman, “Power-law distributions in empirical data,” SIAM Rev.51, 661–703 (2009).
[CrossRef]

P. L. Bollyky, J. B. Bice, I. R. Sweet, B. A. Falk, J. A. Gebe, A. E. Clark, V. H. Gersuk, A. Aderem, T. R. Hawn, and G. T. Nepom, “The toll-like receptor signaling molecule Myd88 contributes to pancreatic beta-cell homeostasis in response to injury.” PloS ONE4, e5063 (2009).
[CrossRef] [PubMed]

S. Hamada, K. Hara, T. Hamada, H. Yasuda, H. Moriyama, R. Nakayama, M. Nagata, and K. Yokono, “Upregulation of the mammalian target of rapamycin complex 1 pathway by ras homolog enriched in brain in pancreatic β-cells leads to increased β-cell mass and prevention of hyperglycemia,” Diabetes58, 1321–1332 (2009).
[CrossRef] [PubMed]

M. Villiger, J. Goulley, M. Friedrich, A. Grapin-Botton, P. Meda, T. Lasser, and R. A. Leitgeb, “In vivo imaging of murine endocrine islets of Langerhans with extended-focus optical coherence microscopy,” Diabetologia52, 1599–1607 (2009).
[CrossRef] [PubMed]

2008 (3)

J. Malcolm, Y. Rathi, A. Yezzi, and A. Tannenbaum, “Fast approximate surface evolution in arbitrary dimension,” Proc. SPIE6914, 69144C (2008).
[CrossRef]

T. Alanentalo, C. E. Lorén, A. Larefalk, J. Sharpe, D. Holmberg, and U. Ahlgren, “High-resolution three-dimensional imaging of islet-infiltrate interactions based on optical projection tomography assessments of the intact adult mouse pancreas,” J. Biomed. Opt.13, 054070 (2008).
[CrossRef] [PubMed]

M. M. Martinic and M. G. von Herrath, “Real-time imaging of the pancreas during development of diabetes,” Immunol. Rev.221, 200–213 (2008).
[CrossRef] [PubMed]

2007 (1)

T. Alanentalo, A. Asayesh, H. Morrison, C. E. Lorén, D. Holmberg, J. Sharpe, and U. Ahlgren, “Tomographic molecular imaging and 3D quantification within adult mouse organs,” Nat. Methods4, 31–33 (2007).
[CrossRef]

2006 (5)

F. Souza, N. Simpson, A. Raffo, C. Saxena, A. Maffei, M. Hardy, M. Kilbourn, R. Goland, R. Leibel, J. Mann, R. Van Heertum, and P. E. Harris, “Longitudinal noninvasive PET-based β cell mass estimates in a spontaneous diabetes rat model,” J. Clin. Invest.116, 1506–1513 (2006).
[CrossRef] [PubMed]

M. Hara, R. F. Dizon, B. S. Glick, C. S. Lee, K. H. Kaestner, D. W. Piston, and V. P. Bindokas, “Imaging pancreatic β-cells in the intact pancreas,” Am. J. Physiol. Endocrinol. Metab.290, E1041–E1047 (2006).
[CrossRef]

P. A. Testoni, B. Mangiavillano, L. Albarello, A. Mariani, P. G. Arcidiacono, E. Masci, and C. Doglioni, “Optical coherence tomography compared with histology of the main pancreatic duct structure in normal and pathological conditions: an ex vivo study,” Digest. Liver Dis.38, 688–695 (2006).
[CrossRef]

Y. Hori, Y. Yasuno, S. Sakai, M. Matsumoto, T. Sugawara, V. D. Madjarova, M. Yamanari, S. Makita, T. Yasui, T. Araki, M. Itoh, and T. Yatagai, “Automatic characterization and segmentation of human skin using three-dimensional optical coherence tomography,” Opt. Express14, 1862–1877 (2006).
[CrossRef] [PubMed]

R. A. Leitgeb, M. Villiger, A. Bachmann, L. Steinmann, and T. Lasser, “Extended focus depth for Fourier domain optical coherence microscopy,” Opt. Lett.31, 2450–2452 (2006).
[CrossRef] [PubMed]

2005 (4)

M. S. Anderson and J. A. Bluestone, “The NOD mouse: a model of immune dysregulation,” Annu. Rev. Immunol.23, 447–485 (2005).
[CrossRef] [PubMed]

T. Bock, B. Pakkenberg, and K. Buschard, “Genetic background determines the size and structure of the endocrine pancreas,” Diabetes54, 133–137 (2005).
[CrossRef]

L. D. Shultz, B. L. Lyons, L. M. Burzenski, B. Gott, X. Chen, S. Chaleff, M. Kotb, S. D. Gillies, M. King, J. Mangada, D. L. Greiner, and R. Handgretinger, “Human lymphoid and myeloid cell development in NOD/LtSz-scid IL2Rγnull mice engrafted with mobilized human hemopoietic stem cells,” J. Immunol.174, 6477–6489 (2005).
[PubMed]

M. Brissova, M. J. Fowler, W. E. Nicholson, A. Chu, B. Hirshberg, D. M. Harlan, and A. C. Powers, “Assessment of human pancreatic islet architecture and composition by laser scanning confocal microscopy.” J. Histochem. Cytochem.53, 1087–1097 (2005).
[CrossRef] [PubMed]

2003 (3)

H. Nagai, “Configurational anatomy of the pancreas: its surgical relevance from ontogenetic and comparative-anatomical viewpoints,” J. Hepatobiliary Pancreat, Surg, (10, 48–56 (2003).

A. F. Fercher, W. Drexler, C. K. Hitzenberger, and T. Lasser, “Optical coherence tomography - principles and applications,” Rep. Prog. Phys.66, 239–303 (2003).
[CrossRef]

J. G. Fujimoto, “Optical coherence tomography for ultrahigh resolution in vivo imaging,” Nat. Biotechnol.21, 1361–1367 (2003).
[CrossRef] [PubMed]

2001 (1)

T. F. Chan and L. A. Vese, “Active contours without edges,” IEEE Trans. Image Process.10, 266–277 (2001).
[CrossRef]

1999 (1)

T. Bock, K. Svenstrup, B. Pakkenberg, and K. Buschard, “Unbiased estimation of total β-cell number and mean β-cell volume in rodent pancreas,” APMIS107, 791–799 (1999).
[CrossRef] [PubMed]

1998 (2)

R. T. Whitaker, “A level-set approach to 3D reconstruction from range data,” Int. J. Comput. Vision29, 203–231 (1998).
[CrossRef]

G. J. Tearney, M. E. Brezinski, J. F. Southern, B. E. Bouma, S. A. Boppart, and J. G. Fujimoto, “Optical biopsy in human pancreatobiliary tissue using optical coherence tomography,” Digest. Dis. Sci.43, 1193–1199 (1998).
[CrossRef] [PubMed]

1994 (1)

Aderem, A.

P. L. Bollyky, J. B. Bice, I. R. Sweet, B. A. Falk, J. A. Gebe, A. E. Clark, V. H. Gersuk, A. Aderem, T. R. Hawn, and G. T. Nepom, “The toll-like receptor signaling molecule Myd88 contributes to pancreatic beta-cell homeostasis in response to injury.” PloS ONE4, e5063 (2009).
[CrossRef] [PubMed]

Ahlgren, U.

A. Hörnblad, A. Cheddad, and U. Ahlgren, “An improved protocol for optical projection tomography imaging reveals lobular heterogeneities in pancreatic islet and β-cell mass distribution,” Islets3, 1–5 (2011).
[CrossRef]

T. Alanentalo, A. Hörnblad, S. Mayans, A. K. Nilsson, J. Sharpe, A. Larefalk, U. Ahlgren, and D. Holmberg, “Quantification and three-dimensional imaging of the insulitis-induced destruction of β-cells in murine type 1 diabetes,” Diabetes59, 1756–1764 (2010).
[CrossRef] [PubMed]

T. Alanentalo, C. E. Lorén, A. Larefalk, J. Sharpe, D. Holmberg, and U. Ahlgren, “High-resolution three-dimensional imaging of islet-infiltrate interactions based on optical projection tomography assessments of the intact adult mouse pancreas,” J. Biomed. Opt.13, 054070 (2008).
[CrossRef] [PubMed]

T. Alanentalo, A. Asayesh, H. Morrison, C. E. Lorén, D. Holmberg, J. Sharpe, and U. Ahlgren, “Tomographic molecular imaging and 3D quantification within adult mouse organs,” Nat. Methods4, 31–33 (2007).
[CrossRef]

Akirav, E. M.

E. M. Akirav, M.-T. Baquero, L. W. Opare-Addo, M. Akirav, E. Galvan, J. A. Kushner, D. L. Rimm, and K. C. Herold, “Glucose and inflammation control islet vascular density and β-cell function in NOD mice: control of islet vasculature and vascular endothelial growth factor by glucose,” Diabetes60, 876–883 (2011).
[CrossRef] [PubMed]

Akirav, M.

E. M. Akirav, M.-T. Baquero, L. W. Opare-Addo, M. Akirav, E. Galvan, J. A. Kushner, D. L. Rimm, and K. C. Herold, “Glucose and inflammation control islet vascular density and β-cell function in NOD mice: control of islet vasculature and vascular endothelial growth factor by glucose,” Diabetes60, 876–883 (2011).
[CrossRef] [PubMed]

Alanentalo, T.

T. Alanentalo, A. Hörnblad, S. Mayans, A. K. Nilsson, J. Sharpe, A. Larefalk, U. Ahlgren, and D. Holmberg, “Quantification and three-dimensional imaging of the insulitis-induced destruction of β-cells in murine type 1 diabetes,” Diabetes59, 1756–1764 (2010).
[CrossRef] [PubMed]

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P.-O. Bastien-Dionne, L. Valenti, N. Kon, W. Gu, and J. Buteau, “Glucagon-like peptide 1 inhibits the sirtuin deacetylase SirT1 to stimulate pancreatic β-cell mass expansion,” Diabetes60, 3217–3222 (2011).
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M. Riopel, M. Krishnamurthy, J. Li, S. Liu, A. Leask, and R. Wang, “Conditional β1-integrin-deficient mice display impaired pancreatic β cell function,” J. Pathol.224, 45–55 (2011).
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Sakai, S.

Saxena, C.

F. Souza, N. Simpson, A. Raffo, C. Saxena, A. Maffei, M. Hardy, M. Kilbourn, R. Goland, R. Leibel, J. Mann, R. Van Heertum, and P. E. Harris, “Longitudinal noninvasive PET-based β cell mass estimates in a spontaneous diabetes rat model,” J. Clin. Invest.116, 1506–1513 (2006).
[CrossRef] [PubMed]

Schroer, S. A.

D. Choi, E. P. Cai, S. A. Schroer, L. Wang, and M. Woo, “Vhl is required for normal pancreatic β cell function and the maintenance of β cell mass with age in mice,” Lab. Invest.91, 527–538 (2011).
[CrossRef] [PubMed]

Sgroi, A.

D. Bosco, M. Armanet, P. Morel, N. Niclauss, A. Sgroi, Y. D. Muller, L. Giovannoni, and T. Berney, “Unique Arrangement of α- and β-cells in Human Islets of Langerhans,” Diabetes59, 1202–1210 (2010).
[CrossRef] [PubMed]

Shalizi, C. R.

A. Clauset, C. R. Shalizi, and M. E. J. Newman, “Power-law distributions in empirical data,” SIAM Rev.51, 661–703 (2009).
[CrossRef]

Sharpe, J.

T. Alanentalo, A. Hörnblad, S. Mayans, A. K. Nilsson, J. Sharpe, A. Larefalk, U. Ahlgren, and D. Holmberg, “Quantification and three-dimensional imaging of the insulitis-induced destruction of β-cells in murine type 1 diabetes,” Diabetes59, 1756–1764 (2010).
[CrossRef] [PubMed]

T. Alanentalo, C. E. Lorén, A. Larefalk, J. Sharpe, D. Holmberg, and U. Ahlgren, “High-resolution three-dimensional imaging of islet-infiltrate interactions based on optical projection tomography assessments of the intact adult mouse pancreas,” J. Biomed. Opt.13, 054070 (2008).
[CrossRef] [PubMed]

T. Alanentalo, A. Asayesh, H. Morrison, C. E. Lorén, D. Holmberg, J. Sharpe, and U. Ahlgren, “Tomographic molecular imaging and 3D quantification within adult mouse organs,” Nat. Methods4, 31–33 (2007).
[CrossRef]

Shultz, L. D.

L. D. Shultz, B. L. Lyons, L. M. Burzenski, B. Gott, X. Chen, S. Chaleff, M. Kotb, S. D. Gillies, M. King, J. Mangada, D. L. Greiner, and R. Handgretinger, “Human lymphoid and myeloid cell development in NOD/LtSz-scid IL2Rγnull mice engrafted with mobilized human hemopoietic stem cells,” J. Immunol.174, 6477–6489 (2005).
[PubMed]

Simpson, N.

F. Souza, N. Simpson, A. Raffo, C. Saxena, A. Maffei, M. Hardy, M. Kilbourn, R. Goland, R. Leibel, J. Mann, R. Van Heertum, and P. E. Harris, “Longitudinal noninvasive PET-based β cell mass estimates in a spontaneous diabetes rat model,” J. Clin. Invest.116, 1506–1513 (2006).
[CrossRef] [PubMed]

Southern, J. F.

G. J. Tearney, M. E. Brezinski, J. F. Southern, B. E. Bouma, S. A. Boppart, and J. G. Fujimoto, “Optical biopsy in human pancreatobiliary tissue using optical coherence tomography,” Digest. Dis. Sci.43, 1193–1199 (1998).
[CrossRef] [PubMed]

Souza, F.

F. Souza, N. Simpson, A. Raffo, C. Saxena, A. Maffei, M. Hardy, M. Kilbourn, R. Goland, R. Leibel, J. Mann, R. Van Heertum, and P. E. Harris, “Longitudinal noninvasive PET-based β cell mass estimates in a spontaneous diabetes rat model,” J. Clin. Invest.116, 1506–1513 (2006).
[CrossRef] [PubMed]

Stangé, G.

M. Chintinne, G. Stangé, B. Denys, P. In ’t Veld, K. Hellemans, M. Pipeleers-Marichal, Z. Ling, and D. Pipeleers, “Contribution of postnatally formed small beta cell aggregates to functional beta cell mass in adult rat pancreas,” Diabetologia53, 2380–2388 (2010).
[CrossRef] [PubMed]

Steinmann, L.

Sugawara, T.

Svenstrup, K.

T. Bock, K. Svenstrup, B. Pakkenberg, and K. Buschard, “Unbiased estimation of total β-cell number and mean β-cell volume in rodent pancreas,” APMIS107, 791–799 (1999).
[CrossRef] [PubMed]

Swanson, E. A.

Sweet, I. R.

P. L. Bollyky, J. B. Bice, I. R. Sweet, B. A. Falk, J. A. Gebe, A. E. Clark, V. H. Gersuk, A. Aderem, T. R. Hawn, and G. T. Nepom, “The toll-like receptor signaling molecule Myd88 contributes to pancreatic beta-cell homeostasis in response to injury.” PloS ONE4, e5063 (2009).
[CrossRef] [PubMed]

Tannenbaum, A.

J. Malcolm, Y. Rathi, A. Yezzi, and A. Tannenbaum, “Fast approximate surface evolution in arbitrary dimension,” Proc. SPIE6914, 69144C (2008).
[CrossRef]

Tatli, S.

Tearney, G. J.

G. J. Tearney, M. E. Brezinski, J. F. Southern, B. E. Bouma, S. A. Boppart, and J. G. Fujimoto, “Optical biopsy in human pancreatobiliary tissue using optical coherence tomography,” Digest. Dis. Sci.43, 1193–1199 (1998).
[CrossRef] [PubMed]

Testoni, P. A.

P. A. Testoni, B. Mangiavillano, L. Albarello, A. Mariani, P. G. Arcidiacono, E. Masci, and C. Doglioni, “Optical coherence tomography compared with histology of the main pancreatic duct structure in normal and pathological conditions: an ex vivo study,” Digest. Liver Dis.38, 688–695 (2006).
[CrossRef]

Tick, S.

I. Ghorbel, F. Rossant, I. Bloch, S. Tick, and M. Paques, “Automated segmentation of macular layers in OCT images and quantitative evaluation of performances,” Pattern Recogn.44, 1590–1603 (2011).
[CrossRef]

Toth, C. A.

Ustun, T.

Valenti, L.

P.-O. Bastien-Dionne, L. Valenti, N. Kon, W. Gu, and J. Buteau, “Glucagon-like peptide 1 inhibits the sirtuin deacetylase SirT1 to stimulate pancreatic β-cell mass expansion,” Diabetes60, 3217–3222 (2011).
[CrossRef] [PubMed]

Van Heertum, R.

F. Souza, N. Simpson, A. Raffo, C. Saxena, A. Maffei, M. Hardy, M. Kilbourn, R. Goland, R. Leibel, J. Mann, R. Van Heertum, and P. E. Harris, “Longitudinal noninvasive PET-based β cell mass estimates in a spontaneous diabetes rat model,” J. Clin. Invest.116, 1506–1513 (2006).
[CrossRef] [PubMed]

Vandsburger, M. H.

P. F. Antkowiak, M. H. Vandsburger, and F. H. Epstein, “Quantitative pancreatic β cell MRI using manganese-enhanced look-locker imaging and two-site water exchange analysis,” Magn. Reson. Med. (Aug.16, 2011) (e-pub ahead of print).
[CrossRef] [PubMed]

Vese, L. A.

T. F. Chan and L. A. Vese, “Active contours without edges,” IEEE Trans. Image Process.10, 266–277 (2001).
[CrossRef]

Villiger, M.

M. Villiger, J. Goulley, E. J. Martin-Williams, A. Grapin-Botton, and T. Lasser, “Towards high resolution optical imaging of beta cells in vivo,” Curr. Pharm. Design16, 1595–1608 (2010).
[CrossRef]

M. Villiger, J. Goulley, M. Friedrich, A. Grapin-Botton, P. Meda, T. Lasser, and R. A. Leitgeb, “In vivo imaging of murine endocrine islets of Langerhans with extended-focus optical coherence microscopy,” Diabetologia52, 1599–1607 (2009).
[CrossRef] [PubMed]

R. A. Leitgeb, M. Villiger, A. Bachmann, L. Steinmann, and T. Lasser, “Extended focus depth for Fourier domain optical coherence microscopy,” Opt. Lett.31, 2450–2452 (2006).
[CrossRef] [PubMed]

von Herrath, M. G.

M. M. Martinic and M. G. von Herrath, “Real-time imaging of the pancreas during development of diabetes,” Immunol. Rev.221, 200–213 (2008).
[CrossRef] [PubMed]

Wang, L.

D. Choi, E. P. Cai, S. A. Schroer, L. Wang, and M. Woo, “Vhl is required for normal pancreatic β cell function and the maintenance of β cell mass with age in mice,” Lab. Invest.91, 527–538 (2011).
[CrossRef] [PubMed]

Wang, R.

M. Riopel, M. Krishnamurthy, J. Li, S. Liu, A. Leask, and R. Wang, “Conditional β1-integrin-deficient mice display impaired pancreatic β cell function,” J. Pathol.224, 45–55 (2011).
[CrossRef] [PubMed]

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R. T. Whitaker, “A level-set approach to 3D reconstruction from range data,” Int. J. Comput. Vision29, 203–231 (1998).
[CrossRef]

Wicki, A.

D. Wild, A. Wicki, R. Mansi, M. Béhé, B. Keil, P. Bernhardt, G. Christofori, P. J. Ell, and H. R. Mäcke, “Exendin-4-based radiopharmaceuticals for glucagonlike peptide-1 receptor PET/CT and SPECT/CT,” J. Nucl. Med.51, 1059–1067 (2010).
[CrossRef] [PubMed]

Wild, D.

D. Wild, A. Wicki, R. Mansi, M. Béhé, B. Keil, P. Bernhardt, G. Christofori, P. J. Ell, and H. R. Mäcke, “Exendin-4-based radiopharmaceuticals for glucagonlike peptide-1 receptor PET/CT and SPECT/CT,” J. Nucl. Med.51, 1059–1067 (2010).
[CrossRef] [PubMed]

Woo, M.

D. Choi, E. P. Cai, S. A. Schroer, L. Wang, and M. Woo, “Vhl is required for normal pancreatic β cell function and the maintenance of β cell mass with age in mice,” Lab. Invest.91, 527–538 (2011).
[CrossRef] [PubMed]

Yamanari, M.

Yasuda, H.

S. Hamada, K. Hara, T. Hamada, H. Yasuda, H. Moriyama, R. Nakayama, M. Nagata, and K. Yokono, “Upregulation of the mammalian target of rapamycin complex 1 pathway by ras homolog enriched in brain in pancreatic β-cells leads to increased β-cell mass and prevention of hyperglycemia,” Diabetes58, 1321–1332 (2009).
[CrossRef] [PubMed]

Yasui, T.

Yasuno, Y.

Yatagai, T.

Yezzi, A.

J. Malcolm, Y. Rathi, A. Yezzi, and A. Tannenbaum, “Fast approximate surface evolution in arbitrary dimension,” Proc. SPIE6914, 69144C (2008).
[CrossRef]

Yokono, K.

S. Hamada, K. Hara, T. Hamada, H. Yasuda, H. Moriyama, R. Nakayama, M. Nagata, and K. Yokono, “Upregulation of the mammalian target of rapamycin complex 1 pathway by ras homolog enriched in brain in pancreatic β-cells leads to increased β-cell mass and prevention of hyperglycemia,” Diabetes58, 1321–1332 (2009).
[CrossRef] [PubMed]

Zhongjie, S.

Y. Lin and S. Zhongjie, “Current views on type 2 diabetes,” J. Endocrinol.204, 1–11 (2010).
[CrossRef]

Am. J. Physiol. Endocrinol. Metab. (1)

M. Hara, R. F. Dizon, B. S. Glick, C. S. Lee, K. H. Kaestner, D. W. Piston, and V. P. Bindokas, “Imaging pancreatic β-cells in the intact pancreas,” Am. J. Physiol. Endocrinol. Metab.290, E1041–E1047 (2006).
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Am. J. Physiol.-Endoc. M. (1)

L. R. Nyman, E. Ford, A. C. Powers, and D. W. Piston, “Glucose-dependent blood flow dynamics in murine pancreatic islets in vivo,” Am. J. Physiol.-Endoc. M.298, E807–E814 (2010).

Annu. Rev. Immunol. (1)

M. S. Anderson and J. A. Bluestone, “The NOD mouse: a model of immune dysregulation,” Annu. Rev. Immunol.23, 447–485 (2005).
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APMIS (1)

T. Bock, K. Svenstrup, B. Pakkenberg, and K. Buschard, “Unbiased estimation of total β-cell number and mean β-cell volume in rodent pancreas,” APMIS107, 791–799 (1999).
[CrossRef] [PubMed]

Biomed. Opt. Express (1)

Curr. Pharm. Design (2)

M. Villiger, J. Goulley, E. J. Martin-Williams, A. Grapin-Botton, and T. Lasser, “Towards high resolution optical imaging of beta cells in vivo,” Curr. Pharm. Design16, 1595–1608 (2010).
[CrossRef]

M. Brom, K. Andralojć, W. J. G. Oyen, O. C. Boerman, and M. Gotthardt, “Development of radiotracers for the determination of the beta-cell mass in vivo,” Curr. Pharm. Design16, 1561–1567 (2010).
[CrossRef]

Diabetes (6)

D. Bosco, M. Armanet, P. Morel, N. Niclauss, A. Sgroi, Y. D. Muller, L. Giovannoni, and T. Berney, “Unique Arrangement of α- and β-cells in Human Islets of Langerhans,” Diabetes59, 1202–1210 (2010).
[CrossRef] [PubMed]

T. Alanentalo, A. Hörnblad, S. Mayans, A. K. Nilsson, J. Sharpe, A. Larefalk, U. Ahlgren, and D. Holmberg, “Quantification and three-dimensional imaging of the insulitis-induced destruction of β-cells in murine type 1 diabetes,” Diabetes59, 1756–1764 (2010).
[CrossRef] [PubMed]

T. Bock, B. Pakkenberg, and K. Buschard, “Genetic background determines the size and structure of the endocrine pancreas,” Diabetes54, 133–137 (2005).
[CrossRef]

E. M. Akirav, M.-T. Baquero, L. W. Opare-Addo, M. Akirav, E. Galvan, J. A. Kushner, D. L. Rimm, and K. C. Herold, “Glucose and inflammation control islet vascular density and β-cell function in NOD mice: control of islet vasculature and vascular endothelial growth factor by glucose,” Diabetes60, 876–883 (2011).
[CrossRef] [PubMed]

P.-O. Bastien-Dionne, L. Valenti, N. Kon, W. Gu, and J. Buteau, “Glucagon-like peptide 1 inhibits the sirtuin deacetylase SirT1 to stimulate pancreatic β-cell mass expansion,” Diabetes60, 3217–3222 (2011).
[CrossRef] [PubMed]

S. Hamada, K. Hara, T. Hamada, H. Yasuda, H. Moriyama, R. Nakayama, M. Nagata, and K. Yokono, “Upregulation of the mammalian target of rapamycin complex 1 pathway by ras homolog enriched in brain in pancreatic β-cells leads to increased β-cell mass and prevention of hyperglycemia,” Diabetes58, 1321–1332 (2009).
[CrossRef] [PubMed]

Diabetes Care (1)

American Diabetes Association“Diagnosis and classification of diabetes mellitus,” Diabetes Care34, S62–S69 (2011).
[PubMed]

Diabetologia (2)

M. Chintinne, G. Stangé, B. Denys, P. In ’t Veld, K. Hellemans, M. Pipeleers-Marichal, Z. Ling, and D. Pipeleers, “Contribution of postnatally formed small beta cell aggregates to functional beta cell mass in adult rat pancreas,” Diabetologia53, 2380–2388 (2010).
[CrossRef] [PubMed]

M. Villiger, J. Goulley, M. Friedrich, A. Grapin-Botton, P. Meda, T. Lasser, and R. A. Leitgeb, “In vivo imaging of murine endocrine islets of Langerhans with extended-focus optical coherence microscopy,” Diabetologia52, 1599–1607 (2009).
[CrossRef] [PubMed]

Digest. Dis. Sci. (1)

G. J. Tearney, M. E. Brezinski, J. F. Southern, B. E. Bouma, S. A. Boppart, and J. G. Fujimoto, “Optical biopsy in human pancreatobiliary tissue using optical coherence tomography,” Digest. Dis. Sci.43, 1193–1199 (1998).
[CrossRef] [PubMed]

Digest. Liver Dis. (1)

P. A. Testoni, B. Mangiavillano, L. Albarello, A. Mariani, P. G. Arcidiacono, E. Masci, and C. Doglioni, “Optical coherence tomography compared with histology of the main pancreatic duct structure in normal and pathological conditions: an ex vivo study,” Digest. Liver Dis.38, 688–695 (2006).
[CrossRef]

IEEE Trans. Image Process. (1)

T. F. Chan and L. A. Vese, “Active contours without edges,” IEEE Trans. Image Process.10, 266–277 (2001).
[CrossRef]

Immunol. Rev. (1)

M. M. Martinic and M. G. von Herrath, “Real-time imaging of the pancreas during development of diabetes,” Immunol. Rev.221, 200–213 (2008).
[CrossRef] [PubMed]

Int. J. Comput. Vision (1)

R. T. Whitaker, “A level-set approach to 3D reconstruction from range data,” Int. J. Comput. Vision29, 203–231 (1998).
[CrossRef]

Islets (1)

A. Hörnblad, A. Cheddad, and U. Ahlgren, “An improved protocol for optical projection tomography imaging reveals lobular heterogeneities in pancreatic islet and β-cell mass distribution,” Islets3, 1–5 (2011).
[CrossRef]

J. Biomed. Opt. (1)

T. Alanentalo, C. E. Lorén, A. Larefalk, J. Sharpe, D. Holmberg, and U. Ahlgren, “High-resolution three-dimensional imaging of islet-infiltrate interactions based on optical projection tomography assessments of the intact adult mouse pancreas,” J. Biomed. Opt.13, 054070 (2008).
[CrossRef] [PubMed]

J. Clin. Invest. (1)

F. Souza, N. Simpson, A. Raffo, C. Saxena, A. Maffei, M. Hardy, M. Kilbourn, R. Goland, R. Leibel, J. Mann, R. Van Heertum, and P. E. Harris, “Longitudinal noninvasive PET-based β cell mass estimates in a spontaneous diabetes rat model,” J. Clin. Invest.116, 1506–1513 (2006).
[CrossRef] [PubMed]

J. Endocrinol. (1)

Y. Lin and S. Zhongjie, “Current views on type 2 diabetes,” J. Endocrinol.204, 1–11 (2010).
[CrossRef]

J. Hepatobiliary Pancreat, Surg (1)

H. Nagai, “Configurational anatomy of the pancreas: its surgical relevance from ontogenetic and comparative-anatomical viewpoints,” J. Hepatobiliary Pancreat, Surg, (10, 48–56 (2003).

J. Histochem. Cytochem. (1)

M. Brissova, M. J. Fowler, W. E. Nicholson, A. Chu, B. Hirshberg, D. M. Harlan, and A. C. Powers, “Assessment of human pancreatic islet architecture and composition by laser scanning confocal microscopy.” J. Histochem. Cytochem.53, 1087–1097 (2005).
[CrossRef] [PubMed]

J. Immunol. (1)

L. D. Shultz, B. L. Lyons, L. M. Burzenski, B. Gott, X. Chen, S. Chaleff, M. Kotb, S. D. Gillies, M. King, J. Mangada, D. L. Greiner, and R. Handgretinger, “Human lymphoid and myeloid cell development in NOD/LtSz-scid IL2Rγnull mice engrafted with mobilized human hemopoietic stem cells,” J. Immunol.174, 6477–6489 (2005).
[PubMed]

J. Nucl. Med. (1)

D. Wild, A. Wicki, R. Mansi, M. Béhé, B. Keil, P. Bernhardt, G. Christofori, P. J. Ell, and H. R. Mäcke, “Exendin-4-based radiopharmaceuticals for glucagonlike peptide-1 receptor PET/CT and SPECT/CT,” J. Nucl. Med.51, 1059–1067 (2010).
[CrossRef] [PubMed]

J. Pathol. (1)

M. Riopel, M. Krishnamurthy, J. Li, S. Liu, A. Leask, and R. Wang, “Conditional β1-integrin-deficient mice display impaired pancreatic β cell function,” J. Pathol.224, 45–55 (2011).
[CrossRef] [PubMed]

Lab. Invest. (1)

D. Choi, E. P. Cai, S. A. Schroer, L. Wang, and M. Woo, “Vhl is required for normal pancreatic β cell function and the maintenance of β cell mass with age in mice,” Lab. Invest.91, 527–538 (2011).
[CrossRef] [PubMed]

Nat. Biotechnol. (1)

J. G. Fujimoto, “Optical coherence tomography for ultrahigh resolution in vivo imaging,” Nat. Biotechnol.21, 1361–1367 (2003).
[CrossRef] [PubMed]

Nat. Methods (1)

T. Alanentalo, A. Asayesh, H. Morrison, C. E. Lorén, D. Holmberg, J. Sharpe, and U. Ahlgren, “Tomographic molecular imaging and 3D quantification within adult mouse organs,” Nat. Methods4, 31–33 (2007).
[CrossRef]

Nature (1)

J. A. Bluestone, K. Herold, and G. Eisenbarth, “Genetics, pathogenesis and clinical interventions in type 1 diabetes,” Nature464, 1293–1300 (2010).
[CrossRef] [PubMed]

Opt. Express (2)

Opt. Lett. (2)

Pattern Recogn. (1)

I. Ghorbel, F. Rossant, I. Bloch, S. Tick, and M. Paques, “Automated segmentation of macular layers in OCT images and quantitative evaluation of performances,” Pattern Recogn.44, 1590–1603 (2011).
[CrossRef]

PloS ONE (1)

P. L. Bollyky, J. B. Bice, I. R. Sweet, B. A. Falk, J. A. Gebe, A. E. Clark, V. H. Gersuk, A. Aderem, T. R. Hawn, and G. T. Nepom, “The toll-like receptor signaling molecule Myd88 contributes to pancreatic beta-cell homeostasis in response to injury.” PloS ONE4, e5063 (2009).
[CrossRef] [PubMed]

Proc. SPIE (1)

J. Malcolm, Y. Rathi, A. Yezzi, and A. Tannenbaum, “Fast approximate surface evolution in arbitrary dimension,” Proc. SPIE6914, 69144C (2008).
[CrossRef]

Rep. Prog. Phys. (1)

A. F. Fercher, W. Drexler, C. K. Hitzenberger, and T. Lasser, “Optical coherence tomography - principles and applications,” Rep. Prog. Phys.66, 239–303 (2003).
[CrossRef]

SIAM Rev. (1)

A. Clauset, C. R. Shalizi, and M. E. J. Newman, “Power-law distributions in empirical data,” SIAM Rev.51, 661–703 (2009).
[CrossRef]

Other (4)

A. C. Davison and D. V. Hinkley, Bootstrap Methods and their Application (Cambridge University Press, 1997).

S. Lankton, “Sparse Field Methods,” Technical Report, Georgia Institute of Technology (July6, 2009).

S. Osher and R. P. Fedkiw, Level Set Methods and Dynamic Implicit Surfaces (Springer-Verlag, 2003).

P. F. Antkowiak, M. H. Vandsburger, and F. H. Epstein, “Quantitative pancreatic β cell MRI using manganese-enhanced look-locker imaging and two-site water exchange analysis,” Magn. Reson. Med. (Aug.16, 2011) (e-pub ahead of print).
[CrossRef] [PubMed]

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Figures (12)

Fig. 1:
Fig. 1:

Schematic layout of the xfOCM setup.

Fig. 2:
Fig. 2:

(a) Schematic representation of the three lobes of a pancreas. (b) Illustration of the experimental procedure.

Fig. 3:
Fig. 3:

Mosaic of 8 en-face views recorded on a pancreatic section. Arrows indicate islets. In addition to the islets, one can clearly observe ducts (arrowhead) and lobe structures. Each picture shows the same area but at different depth positions. (a) 11 μm in depth, (b) 54 μm, (c) 97 μm, (d) 140 μm. Scale bar: 200 μm.

Fig. 4:
Fig. 4:

Schematic 2D representation of the detection principles. The segmentation of the islet is based on an active contours algorithm starting with an initial curve which evolves towards the boundaries of the islet. The active contours algorithm is implemented with the level set method. In this example, the intersection of the grey 3D surface with the plane in blue creates a 2D contour. By moving this plane up (in green) and down (in red), one can make the contour evolve, and even split or merge. The segmentation of the tissue is based on a cluster analysis.

Fig. 5:
Fig. 5:

Flowchart and illustration of the different steps for islet segmentation.

Fig. 6:
Fig. 6:

Flowchart of the TD-algorithm.

Fig. 7:
Fig. 7:

The picture in (a) shows two areas with a higher intensity. By using a three dimensional view, only the area marked by a (*) is defined as an islet by a trained user. However, the result of the algorithm, shown in (b), finds three blobs. The solid arrow shows the correct detection of an islet whereas the dashed arrows indicate false positives. Scale bar: 100 μm.

Fig. 8:
Fig. 8:

Relative error for the extrapolated percentage of β-cell volume per pancreas volume based on different sample sizes. Each red circle represents the results of an individual trial. The black near-horizental line represents the median and the vertical black error bars show the 5th and 95th percentile. Even if the median relative error is below 5% for 5% of the total volume, the spreading error is still of 57% for 25% of the tissue.

Fig. 9:
Fig. 9:

Histogram of the islet volumes in the gastric and duodenal lobes of a 15-week-old female NOD SCID gamma mouse. The islet volume distribution follows a Zipf–Mandelbrot distribution (a). A logarithmic visualization of the size categories shows that the most common islets are those of volume less than 8000 μm3, followed by those between 32000 and 64000 μm3 (b). The proportion of the islet volume of each size category to the total β-cell volume is inversely related to their occurrences, with the smallest categories of 8000 μm3 contributing only 3% (c).

Fig. 10:
Fig. 10:

(a) Histogram distribution of the duodenal lobe and (b) of the gastric lobe. The Zipf-Mandelbrot parameters are N = 600, q = 0.56 and s = 1.65 for the duodenal lobe and N = 600, q = 0.38 and s = 1.41 for the gastric lobe, with a p-value of 0.11 and 0.7, respectively.

Fig. 11:
Fig. 11:

Success rate to detect a deviation between the healthy and simulated sick datasets for scenarios A and B. The colorbar indicates the proportion of successful detection over 2500 trials.

Fig. 12:
Fig. 12:

Success rate for discrimination between the healthy and simulated sick datasets for scenario C. Each diagram represents a probability (p) of an islet being attacked. The x-axis shows the probabilities that if an islet is attacked the individual cell will be destroyed. The y-axis always represents the different sample size used to do the test. The colorbar indicates the proportion of successful detection over 2500 trials.

Equations (1)

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f ( k ; N , q , s ) = ( k + q ) s i = 1 N ( i + q ) s , k = 1 , , N ,

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