Abstract

Alzheimer’s disease (AD) is characterized by amyloidosis of brain tissues. This phenomenon is studied with genetically-modified mouse models. We propose a method to quantify amyloidosis in whole 5xFAD mouse brains, a model of AD. We use optical projection tomography (OPT) and a random forest voxel classifier to segment and measure amyloid plaques. We validate our method in a preliminary cross-sectional study, where we measure 6136 ± 1637, 8477 ± 3438, and 17267 ± 4241 plaques (AVG ± SD) at 11, 17, and 31 weeks. Overall, this method can be used in the evaluation of new treatments against AD.

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

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2019 (2)

T. Falk, D. Mai, R. Bensch, Ö. Çiçek, A. Abdulkadir, Y. Marrakchi, A. Böhm, J. Deubner, Z. Jäckel, K. Seiwald, A. Dovzhenko, O. Tietz, C. Dal Bosco, S. Walsh, D. Saltukoglu, T. L. Tay, M. Prinz, K. Palme, M. Simons, I. Diester, T. Brox, and O. Ronneberger, “U-net: deep learning for cell counting, detection, and morphometry,” Nat. methods 16, 67 (2019).
[Crossref]

J. C. C. Hernández, O. Bracko, C. J. Kersbergen, V. Muse, M. Haft-Javaherian, M. Berg, L. Park, L. K. Vinarcsik, I. Ivasyk, D. A. Rivera, Y. Kang, M. Coertes-Canteli, M. Peyrounette, V. Doyeux, A. Smith, J. Zhou, G. Otte, J. D. Beverly, E. Davenport, Y. Davit, C. P. Lin, S. Strickland, C. Iadecola, S. Lorthois, N. Nishimura, and C. B. Schaffer, “Neutrophil adhesion in brain capillaries reduces cortical blood flow and impairs memory function in alzheimer’s disease mouse models,” Nat. Neuroscience 2019, 1 (2019).

2018 (1)

J. D. Whitesell, A. R. Buckley, J. E. Knox, L. Kuan, N. Graddis, A. Pelos, A. Mukora, W. Wakeman, P. Bohn, A. Ho, K. E. Hirokawa, and J. A. Harris, “Whole brain imaging reveals distinct spatial patterns of amyloid beta deposition in three mouse models of Alzheimer’s disease,” J. Comp. Neurol. 2018, 24555 (2018).
[Crossref]

2017 (5)

S. Han, M. Kollmer, D. Markx, S. Claus, P. Walther, and M. Fändrich, “Amyloid plaque structure and cell surface interactions of β-amyloid fibrils revealed by electron tomography,” Sci. reports 7, 43577 (2017).
[Crossref]

R. H. Takahashi, T. Nagao, and G. K. Gouras, “Plaque formation and the intraneuronal accumulation of β-amyloid in alzheimer’s disease,” Pathol. international 67, 185–193 (2017).
[Crossref]

C. Dudeffant, M. Vandesquille, K. Herbert, C. M. Garin, S. Alves, V. Blanchard, E. E. Comoy, F. Petit, and M. Dhenain, “Contrast-enhanced mr microscopy of amyloid plaques in five mouse models of amyloidosis and in human alzheimer’s disease brains,” Sci. reports 7, 4955 (2017).
[Crossref]

A. Ebadi, J. L. Dalboni da Rocha, D. B. Nagaraju, F. Tovar-Moll, I. Bramati, G. Coutinho, R. Sitaram, and P. Rashidi, “Ensemble classification of alzheimer’s disease and mild cognitive impairment based on complex graph measures from diffusion tensor images,” Front. neuroscience 11, 56 (2017).
[Crossref]

D. Nguyen, P. J. Marchand, A. L. Planchette, J. Nilsson, M. Sison, J. Extermann, A. Lopez, M. Sylwestrzak, J. Sordet-Dessimoz, A. Schmidt-Christensen, D. Holmberg, D. Van De Ville, and T. Lasser, “Optical projection tomography for rapid whole mouse brain imaging,” Biomed. optics express 8, 5637–5650 (2017).
[Crossref]

2016 (6)

F. Letronne, G. Laumet, A.-M. Ayral, J. Chapuis, F. Demiautte, M. Laga, M. E. Vandenberghe, N. Malmanche, F. Leroux, F. Eysert, Y. Sottejeau, L. Chami, A. Flaig, C. Bauer, P. Dourlen, M. Lesaffre, C. Delay, L. Huot, J. Dumont, E. Werkmeister, F. Lafont, T. Mendes, F. Hansmannel, B. Dermaut, B. Deprez, A.-S. Hérard, M. Dhenain, N. Souedet, F. Pasquier, D. Tulasne, C. Berr, J.-J. Hauw, Y. Lemoine, P. Amouyel, D. Mann, R. Déprez, F. Checler, D. Hot, T. Delzescaux, K. Gevaert, and J.-C. Lambert, “Adam30 downregulates app-linked defects through cathepsin d activation in alzheimer’s disease,” EBioMedicine 9, 278–292 (2016).
[Crossref] [PubMed]

M. E. Vandenberghe, A.-S. Hérard, N. Souedet, E. Sadouni, M. D. Santin, D. Briet, D. Carré, J. Schulz, P. Hantraye, P.-E. Chabrier, T. Rooney, T. Debeir, V. Blanchard, L. Pradier, M. Dhenain, and T. Delzescaux, “High-throughput 3d whole-brain quantitative histopathology in rodents,” Sci. reports 6, 20958 (2016).
[Crossref]

Y. Li, X. Zhu, P. Wang, J. Wang, S. Liu, F. Li, and M. Qiu, “Detection of aβ plaque deposition in mr images based on pixel feature selection and class information in image level,” Biomed. engineering online 15, 108 (2016).
[Crossref]

P. Strnad, S. Gunther, J. Reichmann, U. Krzic, B. Balazs, G. De Medeiros, N. Norlin, T. Hiiragi, L. Hufnagel, and J. Ellenberg, “Inverted light-sheet microscope for imaging mouse pre-implantation development,” Nat. methods 13, 139 (2016).
[Crossref]

S. Beker, V. Kellner, G. Chechik, and E. A. Stern, “Learning to classify neural activity from a mouse model of alzheimer’s disease amyloidosis versus controls,” Alzheimer’s & Dementia: Diagn. Assess. & Dis. Monit. 2, 39–48 (2016).

T. Liebmann, N. Renier, K. Bettayeb, P. Greengard, M. Tessier-Lavigne, and M. Flajolet, “Three-dimensional study of alzheimer’s disease hallmarks using the idisco clearing method,” Cell reports 16, 1138–1152 (2016).
[Crossref]

2015 (2)

N. Jährling, K. Becker, B. M. Wegenast-Braun, S. A. Grathwohl, M. Jucker, and H.-U. Dodt, “Cerebral β-amyloidosis in mice investigated by ultramicroscopy,” PloS one 10, e0125418 (2015).
[Crossref]

L. Kuan, Y. Li, C. Lau, D. Feng, A. Bernard, S. M. Sunkin, H. Zeng, C. Dang, M. Hawrylycz, and L. Ng, “Neuroinformatics of the allen mouse brain connectivity atlas,” Methods 73, 4–17 (2015).
[Crossref]

2014 (2)

M. Fernández-Delgado, E. Cernadas, S. Barro, and D. Amorim, “Do we need hundreds of classifiers to solve real world classification problems?” The J. Mach. Learn. Res. 15, 3133–3181 (2014).

A. Depeursinge, A. Foncubierta-Rodriguez, D. Van De Ville, and H. Müller, “Three-dimensional solid texture analysis in biomedical imaging: review and opportunities,” Med. image analysis 18, 176–196 (2014).
[Crossref]

2013 (3)

M. Grand’Maison, S. P. Zehntner, M.-K. Ho, F. Hébert, A. Wood, F. Carbonell, A. P. Zijdenbos, E. Hamel, and B. J. Bedell, “Early cortical thickness changes predict β-amyloid deposition in a mouse model of alzheimer’s disease,” Neurobiol. disease 54, 59–67 (2013).
[Crossref]

D. Dong, S. Zhu, C. Qin, V. Kumar, J. V. Stein, S. Oehler, C. Savakis, J. Tian, and J. Ripoll, “Automated recovery of the center of rotation in optical projection tomography in the presence of scattering,” IEEE journal biomedical health informatics 17, 198–204 (2013).
[Crossref]

K. R. Gray, P. Aljabar, R. A. Heckemann, A. Hammers, D. Rueckert, and A. D. N. Initiative, “Random forest-based similarity measures for multi-modal classification of alzheimer’s disease,” NeuroImage 65, 167–175 (2013).
[Crossref]

2012 (5)

T. Bolmont, A. Bouwens, C. Pache, M. Dimitrov, C. Berclaz, M. Villiger, B. M. Wegenast-Braun, T. Lasser, and P. C. Fraering, “Label-free imaging of cerebral β-amyloidosis with extended-focus optical coherence microscopy,” J. Neurosci. 32, 14548–14556 (2012).
[Crossref] [PubMed]

B. Pinzer, M. Cacquevel, P. Modregger, S. McDonald, J. Bensadoun, T. Thuering, P. Aebischer, and M. Stampanoni, “Imaging brain amyloid deposition using grating-based differential phase contrast tomography,” Neuroimage 61, 1336–1346 (2012).
[Crossref] [PubMed]

J. Schindelin, I. Arganda-Carreras, E. Frise, V. Kaynig, M. Longair, T. Pietzsch, S. Preibisch, C. Rueden, S. Saalfeld, B. Schmid, J.-Y. Tinevez, D. J. White, V. Hartenstein, K. Eliceiri, P. Tomancak, and A. Cardona, “Fiji: an open-source platform for biological-image analysis,” Nat. methods 9, 676 (2012).
[Crossref] [PubMed]

J. A. Gleave, M. D. Wong, J. Dazai, M. Altaf, R. Mark Henkelman, J. P. Lerch, and B. J. Nieman, “Neuroanatomical phenotyping of the mouse brain with three-dimensional autofluorescence imaging,” Physiol. genomics 44, 778–785 (2012).
[Crossref] [PubMed]

G. Iordanescu, P. N. Venkatasubramanian, and A. M. Wyrwicz, “Automatic segmentation of amyloid plaques in mr images using unsupervised support vector machines,” Magn. resonance medicine 67, 1794–1802 (2012).
[Crossref]

2011 (2)

L. Shamir, “Assessing the efficacy of low-level image content descriptors for computer-based fluorescence microscopy image analysis,” J. microscopy 243, 284–292 (2011).
[Crossref]

A. Kreshuk, C. N. Straehle, C. Sommer, U. Koethe, M. Cantoni, G. Knott, and F. A. Hamprecht, “Automated detection and segmentation of synaptic contacts in nearly isotropic serial electron microscopy images,” PloS one 6, e24899 (2011).
[Crossref] [PubMed]

2009 (2)

P. Yan, A. W. Bero, J. R. Cirrito, Q. Xiao, X. Hu, Y. Wang, E. Gonzales, D. M. Holtzman, and J.-M. Lee, “Characterizing the appearance and growth of amyloid plaques in app/ps1 mice,” J. Neurosci. 29, 10706–10714 (2009).
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S. Hu, P. Yan, K. Maslov, J.-M. Lee, and L. V. Wang, “Intravital imaging of amyloid plaques in a transgenic mouse model using optical-resolution photoacoustic microscopy,” Opt. letters 34, 3899–3901 (2009).
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2008 (1)

C. A. Raji, J. T. Becker, N. D. Tsopelas, J. C. Price, C. A. Mathis, J. A. Saxton, B. J. Lopresti, J. A. Hoge, S. K. Ziolko, S. T. DeKosky, and W. E. Klunk, “Characterizing regional correlation, laterality and symmetry of amyloid deposition in mild cognitive impairment and alzheimer’s disease with pittsburgh compound b,” J. neuroscience methods 172, 277–282 (2008).
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2006 (2)

H. Oakley, S. L. Cole, S. Logan, E. Maus, P. Shao, J. Craft, A. Guillozet-Bongaarts, M. Ohno, J. Disterhoft, L. Van Eldik, R. Berry, and R. Vassar, “Intraneuronal β-amyloid aggregates, neurodegeneration, and neuron loss in transgenic mice with five familial alzheimer’s disease mutations: potential factors in amyloid plaque formation,” J. Neurosci. 26, 10129–10140 (2006).
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C. Chubb, Y. Inagaki, P. Sheu, B. Cummings, A. Wasserman, E. Head, and C. Cotman, “Biovision: an application for the automated image analysis of histological sections,” Neurobiol. aging 27, 1462–1476 (2006).
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2004 (1)

J. Götz, J. Streffer, D. David, A. Schild, F. Hoerndli, L. Pennanen, P. Kurosinski, and F. Chen, “Transgenic animal models of alzheimer’s disease and related disorders: histopathology, behavior and therapy,” Mol. psychiatry 9, 664 (2004).
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2002 (3)

J. Hardy and D. J. Selkoe, “The amyloid hypothesis of alzheimer’s disease: progress and problems on the road to therapeutics,” science 297, 353–356 (2002).
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J. Sharpe, U. Ahlgren, P. Perry, B. Hill, A. Ross, J. Hecksher-Sørensen, R. Baldock, and D. Davidson, “Optical projection tomography as a tool for 3d microscopy and gene expression studies,” Science 296, 541–545 (2002).
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W. E. Klunk, B. J. Bacskai, C. A. Mathis, S. T. Kajdasz, M. E. McLellan, M. P. Frosch, M. L. Debnath, D. P. Holt, Y. Wang, and B. T. Hyman, “Imaging aβ plaques in living transgenic mice with multiphoton microscopy and methoxy-x04, a systemically administered congo red derivative,” J. Neuropathol. & Exp. Neurol. 61, 797–805 (2002).
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2001 (1)

L. Breiman, “Random forests,” Mach. learning 45, 5–32 (2001).
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Abdulkadir, A.

T. Falk, D. Mai, R. Bensch, Ö. Çiçek, A. Abdulkadir, Y. Marrakchi, A. Böhm, J. Deubner, Z. Jäckel, K. Seiwald, A. Dovzhenko, O. Tietz, C. Dal Bosco, S. Walsh, D. Saltukoglu, T. L. Tay, M. Prinz, K. Palme, M. Simons, I. Diester, T. Brox, and O. Ronneberger, “U-net: deep learning for cell counting, detection, and morphometry,” Nat. methods 16, 67 (2019).
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Aebischer, P.

B. Pinzer, M. Cacquevel, P. Modregger, S. McDonald, J. Bensadoun, T. Thuering, P. Aebischer, and M. Stampanoni, “Imaging brain amyloid deposition using grating-based differential phase contrast tomography,” Neuroimage 61, 1336–1346 (2012).
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Ahlgren, U.

J. Sharpe, U. Ahlgren, P. Perry, B. Hill, A. Ross, J. Hecksher-Sørensen, R. Baldock, and D. Davidson, “Optical projection tomography as a tool for 3d microscopy and gene expression studies,” Science 296, 541–545 (2002).
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Aljabar, P.

K. R. Gray, P. Aljabar, R. A. Heckemann, A. Hammers, D. Rueckert, and A. D. N. Initiative, “Random forest-based similarity measures for multi-modal classification of alzheimer’s disease,” NeuroImage 65, 167–175 (2013).
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Altaf, M.

J. A. Gleave, M. D. Wong, J. Dazai, M. Altaf, R. Mark Henkelman, J. P. Lerch, and B. J. Nieman, “Neuroanatomical phenotyping of the mouse brain with three-dimensional autofluorescence imaging,” Physiol. genomics 44, 778–785 (2012).
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Alves, S.

C. Dudeffant, M. Vandesquille, K. Herbert, C. M. Garin, S. Alves, V. Blanchard, E. E. Comoy, F. Petit, and M. Dhenain, “Contrast-enhanced mr microscopy of amyloid plaques in five mouse models of amyloidosis and in human alzheimer’s disease brains,” Sci. reports 7, 4955 (2017).
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Amorim, D.

M. Fernández-Delgado, E. Cernadas, S. Barro, and D. Amorim, “Do we need hundreds of classifiers to solve real world classification problems?” The J. Mach. Learn. Res. 15, 3133–3181 (2014).

Amouyel, P.

F. Letronne, G. Laumet, A.-M. Ayral, J. Chapuis, F. Demiautte, M. Laga, M. E. Vandenberghe, N. Malmanche, F. Leroux, F. Eysert, Y. Sottejeau, L. Chami, A. Flaig, C. Bauer, P. Dourlen, M. Lesaffre, C. Delay, L. Huot, J. Dumont, E. Werkmeister, F. Lafont, T. Mendes, F. Hansmannel, B. Dermaut, B. Deprez, A.-S. Hérard, M. Dhenain, N. Souedet, F. Pasquier, D. Tulasne, C. Berr, J.-J. Hauw, Y. Lemoine, P. Amouyel, D. Mann, R. Déprez, F. Checler, D. Hot, T. Delzescaux, K. Gevaert, and J.-C. Lambert, “Adam30 downregulates app-linked defects through cathepsin d activation in alzheimer’s disease,” EBioMedicine 9, 278–292 (2016).
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Arganda-Carreras, I.

J. Schindelin, I. Arganda-Carreras, E. Frise, V. Kaynig, M. Longair, T. Pietzsch, S. Preibisch, C. Rueden, S. Saalfeld, B. Schmid, J.-Y. Tinevez, D. J. White, V. Hartenstein, K. Eliceiri, P. Tomancak, and A. Cardona, “Fiji: an open-source platform for biological-image analysis,” Nat. methods 9, 676 (2012).
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Ayral, A.-M.

F. Letronne, G. Laumet, A.-M. Ayral, J. Chapuis, F. Demiautte, M. Laga, M. E. Vandenberghe, N. Malmanche, F. Leroux, F. Eysert, Y. Sottejeau, L. Chami, A. Flaig, C. Bauer, P. Dourlen, M. Lesaffre, C. Delay, L. Huot, J. Dumont, E. Werkmeister, F. Lafont, T. Mendes, F. Hansmannel, B. Dermaut, B. Deprez, A.-S. Hérard, M. Dhenain, N. Souedet, F. Pasquier, D. Tulasne, C. Berr, J.-J. Hauw, Y. Lemoine, P. Amouyel, D. Mann, R. Déprez, F. Checler, D. Hot, T. Delzescaux, K. Gevaert, and J.-C. Lambert, “Adam30 downregulates app-linked defects through cathepsin d activation in alzheimer’s disease,” EBioMedicine 9, 278–292 (2016).
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Bacskai, B. J.

W. E. Klunk, B. J. Bacskai, C. A. Mathis, S. T. Kajdasz, M. E. McLellan, M. P. Frosch, M. L. Debnath, D. P. Holt, Y. Wang, and B. T. Hyman, “Imaging aβ plaques in living transgenic mice with multiphoton microscopy and methoxy-x04, a systemically administered congo red derivative,” J. Neuropathol. & Exp. Neurol. 61, 797–805 (2002).
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P. Strnad, S. Gunther, J. Reichmann, U. Krzic, B. Balazs, G. De Medeiros, N. Norlin, T. Hiiragi, L. Hufnagel, and J. Ellenberg, “Inverted light-sheet microscope for imaging mouse pre-implantation development,” Nat. methods 13, 139 (2016).
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Baldock, R.

J. Sharpe, U. Ahlgren, P. Perry, B. Hill, A. Ross, J. Hecksher-Sørensen, R. Baldock, and D. Davidson, “Optical projection tomography as a tool for 3d microscopy and gene expression studies,” Science 296, 541–545 (2002).
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Barro, S.

M. Fernández-Delgado, E. Cernadas, S. Barro, and D. Amorim, “Do we need hundreds of classifiers to solve real world classification problems?” The J. Mach. Learn. Res. 15, 3133–3181 (2014).

Bauer, C.

F. Letronne, G. Laumet, A.-M. Ayral, J. Chapuis, F. Demiautte, M. Laga, M. E. Vandenberghe, N. Malmanche, F. Leroux, F. Eysert, Y. Sottejeau, L. Chami, A. Flaig, C. Bauer, P. Dourlen, M. Lesaffre, C. Delay, L. Huot, J. Dumont, E. Werkmeister, F. Lafont, T. Mendes, F. Hansmannel, B. Dermaut, B. Deprez, A.-S. Hérard, M. Dhenain, N. Souedet, F. Pasquier, D. Tulasne, C. Berr, J.-J. Hauw, Y. Lemoine, P. Amouyel, D. Mann, R. Déprez, F. Checler, D. Hot, T. Delzescaux, K. Gevaert, and J.-C. Lambert, “Adam30 downregulates app-linked defects through cathepsin d activation in alzheimer’s disease,” EBioMedicine 9, 278–292 (2016).
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Becker, J. T.

C. A. Raji, J. T. Becker, N. D. Tsopelas, J. C. Price, C. A. Mathis, J. A. Saxton, B. J. Lopresti, J. A. Hoge, S. K. Ziolko, S. T. DeKosky, and W. E. Klunk, “Characterizing regional correlation, laterality and symmetry of amyloid deposition in mild cognitive impairment and alzheimer’s disease with pittsburgh compound b,” J. neuroscience methods 172, 277–282 (2008).
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Becker, K.

N. Jährling, K. Becker, B. M. Wegenast-Braun, S. A. Grathwohl, M. Jucker, and H.-U. Dodt, “Cerebral β-amyloidosis in mice investigated by ultramicroscopy,” PloS one 10, e0125418 (2015).
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K. Becker, N. Jährling, S. Saghafi, and H.-U. Dodt, “Ultramicroscopy: light-sheet-based microscopy for imaging centimeter-sized objects with micrometer resolution,” Cold Spring Harb. protocols2013, pdb–top076539 (2013).

Bedell, B. J.

M. Grand’Maison, S. P. Zehntner, M.-K. Ho, F. Hébert, A. Wood, F. Carbonell, A. P. Zijdenbos, E. Hamel, and B. J. Bedell, “Early cortical thickness changes predict β-amyloid deposition in a mouse model of alzheimer’s disease,” Neurobiol. disease 54, 59–67 (2013).
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Beker, S.

S. Beker, V. Kellner, G. Chechik, and E. A. Stern, “Learning to classify neural activity from a mouse model of alzheimer’s disease amyloidosis versus controls,” Alzheimer’s & Dementia: Diagn. Assess. & Dis. Monit. 2, 39–48 (2016).

Bensadoun, J.

B. Pinzer, M. Cacquevel, P. Modregger, S. McDonald, J. Bensadoun, T. Thuering, P. Aebischer, and M. Stampanoni, “Imaging brain amyloid deposition using grating-based differential phase contrast tomography,” Neuroimage 61, 1336–1346 (2012).
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Bensch, R.

T. Falk, D. Mai, R. Bensch, Ö. Çiçek, A. Abdulkadir, Y. Marrakchi, A. Böhm, J. Deubner, Z. Jäckel, K. Seiwald, A. Dovzhenko, O. Tietz, C. Dal Bosco, S. Walsh, D. Saltukoglu, T. L. Tay, M. Prinz, K. Palme, M. Simons, I. Diester, T. Brox, and O. Ronneberger, “U-net: deep learning for cell counting, detection, and morphometry,” Nat. methods 16, 67 (2019).
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Berclaz, C.

T. Bolmont, A. Bouwens, C. Pache, M. Dimitrov, C. Berclaz, M. Villiger, B. M. Wegenast-Braun, T. Lasser, and P. C. Fraering, “Label-free imaging of cerebral β-amyloidosis with extended-focus optical coherence microscopy,” J. Neurosci. 32, 14548–14556 (2012).
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Berg, M.

J. C. C. Hernández, O. Bracko, C. J. Kersbergen, V. Muse, M. Haft-Javaherian, M. Berg, L. Park, L. K. Vinarcsik, I. Ivasyk, D. A. Rivera, Y. Kang, M. Coertes-Canteli, M. Peyrounette, V. Doyeux, A. Smith, J. Zhou, G. Otte, J. D. Beverly, E. Davenport, Y. Davit, C. P. Lin, S. Strickland, C. Iadecola, S. Lorthois, N. Nishimura, and C. B. Schaffer, “Neutrophil adhesion in brain capillaries reduces cortical blood flow and impairs memory function in alzheimer’s disease mouse models,” Nat. Neuroscience 2019, 1 (2019).

Bernard, A.

L. Kuan, Y. Li, C. Lau, D. Feng, A. Bernard, S. M. Sunkin, H. Zeng, C. Dang, M. Hawrylycz, and L. Ng, “Neuroinformatics of the allen mouse brain connectivity atlas,” Methods 73, 4–17 (2015).
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Bero, A. W.

P. Yan, A. W. Bero, J. R. Cirrito, Q. Xiao, X. Hu, Y. Wang, E. Gonzales, D. M. Holtzman, and J.-M. Lee, “Characterizing the appearance and growth of amyloid plaques in app/ps1 mice,” J. Neurosci. 29, 10706–10714 (2009).
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Berr, C.

F. Letronne, G. Laumet, A.-M. Ayral, J. Chapuis, F. Demiautte, M. Laga, M. E. Vandenberghe, N. Malmanche, F. Leroux, F. Eysert, Y. Sottejeau, L. Chami, A. Flaig, C. Bauer, P. Dourlen, M. Lesaffre, C. Delay, L. Huot, J. Dumont, E. Werkmeister, F. Lafont, T. Mendes, F. Hansmannel, B. Dermaut, B. Deprez, A.-S. Hérard, M. Dhenain, N. Souedet, F. Pasquier, D. Tulasne, C. Berr, J.-J. Hauw, Y. Lemoine, P. Amouyel, D. Mann, R. Déprez, F. Checler, D. Hot, T. Delzescaux, K. Gevaert, and J.-C. Lambert, “Adam30 downregulates app-linked defects through cathepsin d activation in alzheimer’s disease,” EBioMedicine 9, 278–292 (2016).
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Berry, R.

H. Oakley, S. L. Cole, S. Logan, E. Maus, P. Shao, J. Craft, A. Guillozet-Bongaarts, M. Ohno, J. Disterhoft, L. Van Eldik, R. Berry, and R. Vassar, “Intraneuronal β-amyloid aggregates, neurodegeneration, and neuron loss in transgenic mice with five familial alzheimer’s disease mutations: potential factors in amyloid plaque formation,” J. Neurosci. 26, 10129–10140 (2006).
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Bettayeb, K.

T. Liebmann, N. Renier, K. Bettayeb, P. Greengard, M. Tessier-Lavigne, and M. Flajolet, “Three-dimensional study of alzheimer’s disease hallmarks using the idisco clearing method,” Cell reports 16, 1138–1152 (2016).
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Beverly, J. D.

J. C. C. Hernández, O. Bracko, C. J. Kersbergen, V. Muse, M. Haft-Javaherian, M. Berg, L. Park, L. K. Vinarcsik, I. Ivasyk, D. A. Rivera, Y. Kang, M. Coertes-Canteli, M. Peyrounette, V. Doyeux, A. Smith, J. Zhou, G. Otte, J. D. Beverly, E. Davenport, Y. Davit, C. P. Lin, S. Strickland, C. Iadecola, S. Lorthois, N. Nishimura, and C. B. Schaffer, “Neutrophil adhesion in brain capillaries reduces cortical blood flow and impairs memory function in alzheimer’s disease mouse models,” Nat. Neuroscience 2019, 1 (2019).

Blanchard, V.

C. Dudeffant, M. Vandesquille, K. Herbert, C. M. Garin, S. Alves, V. Blanchard, E. E. Comoy, F. Petit, and M. Dhenain, “Contrast-enhanced mr microscopy of amyloid plaques in five mouse models of amyloidosis and in human alzheimer’s disease brains,” Sci. reports 7, 4955 (2017).
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M. E. Vandenberghe, A.-S. Hérard, N. Souedet, E. Sadouni, M. D. Santin, D. Briet, D. Carré, J. Schulz, P. Hantraye, P.-E. Chabrier, T. Rooney, T. Debeir, V. Blanchard, L. Pradier, M. Dhenain, and T. Delzescaux, “High-throughput 3d whole-brain quantitative histopathology in rodents,” Sci. reports 6, 20958 (2016).
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Böhm, A.

T. Falk, D. Mai, R. Bensch, Ö. Çiçek, A. Abdulkadir, Y. Marrakchi, A. Böhm, J. Deubner, Z. Jäckel, K. Seiwald, A. Dovzhenko, O. Tietz, C. Dal Bosco, S. Walsh, D. Saltukoglu, T. L. Tay, M. Prinz, K. Palme, M. Simons, I. Diester, T. Brox, and O. Ronneberger, “U-net: deep learning for cell counting, detection, and morphometry,” Nat. methods 16, 67 (2019).
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Bohn, P.

J. D. Whitesell, A. R. Buckley, J. E. Knox, L. Kuan, N. Graddis, A. Pelos, A. Mukora, W. Wakeman, P. Bohn, A. Ho, K. E. Hirokawa, and J. A. Harris, “Whole brain imaging reveals distinct spatial patterns of amyloid beta deposition in three mouse models of Alzheimer’s disease,” J. Comp. Neurol. 2018, 24555 (2018).
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Bolmont, T.

T. Bolmont, A. Bouwens, C. Pache, M. Dimitrov, C. Berclaz, M. Villiger, B. M. Wegenast-Braun, T. Lasser, and P. C. Fraering, “Label-free imaging of cerebral β-amyloidosis with extended-focus optical coherence microscopy,” J. Neurosci. 32, 14548–14556 (2012).
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Bouwens, A.

T. Bolmont, A. Bouwens, C. Pache, M. Dimitrov, C. Berclaz, M. Villiger, B. M. Wegenast-Braun, T. Lasser, and P. C. Fraering, “Label-free imaging of cerebral β-amyloidosis with extended-focus optical coherence microscopy,” J. Neurosci. 32, 14548–14556 (2012).
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O. Teboul, A. Feki, A. Dubois, B. Bozon, A. Faure, P. Hantraye, M. Dhenain, B. Delatour, and T. Delzescaux, “A standardized method to automatically segment amyloid plaques in congo red stained sections from alzheimer transgenic mice,” in 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, (IEEE, 2007), pp. 5593–5596.
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J. C. C. Hernández, O. Bracko, C. J. Kersbergen, V. Muse, M. Haft-Javaherian, M. Berg, L. Park, L. K. Vinarcsik, I. Ivasyk, D. A. Rivera, Y. Kang, M. Coertes-Canteli, M. Peyrounette, V. Doyeux, A. Smith, J. Zhou, G. Otte, J. D. Beverly, E. Davenport, Y. Davit, C. P. Lin, S. Strickland, C. Iadecola, S. Lorthois, N. Nishimura, and C. B. Schaffer, “Neutrophil adhesion in brain capillaries reduces cortical blood flow and impairs memory function in alzheimer’s disease mouse models,” Nat. Neuroscience 2019, 1 (2019).

Bramati, I.

A. Ebadi, J. L. Dalboni da Rocha, D. B. Nagaraju, F. Tovar-Moll, I. Bramati, G. Coutinho, R. Sitaram, and P. Rashidi, “Ensemble classification of alzheimer’s disease and mild cognitive impairment based on complex graph measures from diffusion tensor images,” Front. neuroscience 11, 56 (2017).
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L. Breiman, “Random forests,” Mach. learning 45, 5–32 (2001).
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M. E. Vandenberghe, A.-S. Hérard, N. Souedet, E. Sadouni, M. D. Santin, D. Briet, D. Carré, J. Schulz, P. Hantraye, P.-E. Chabrier, T. Rooney, T. Debeir, V. Blanchard, L. Pradier, M. Dhenain, and T. Delzescaux, “High-throughput 3d whole-brain quantitative histopathology in rodents,” Sci. reports 6, 20958 (2016).
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Brox, T.

T. Falk, D. Mai, R. Bensch, Ö. Çiçek, A. Abdulkadir, Y. Marrakchi, A. Böhm, J. Deubner, Z. Jäckel, K. Seiwald, A. Dovzhenko, O. Tietz, C. Dal Bosco, S. Walsh, D. Saltukoglu, T. L. Tay, M. Prinz, K. Palme, M. Simons, I. Diester, T. Brox, and O. Ronneberger, “U-net: deep learning for cell counting, detection, and morphometry,” Nat. methods 16, 67 (2019).
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J. D. Whitesell, A. R. Buckley, J. E. Knox, L. Kuan, N. Graddis, A. Pelos, A. Mukora, W. Wakeman, P. Bohn, A. Ho, K. E. Hirokawa, and J. A. Harris, “Whole brain imaging reveals distinct spatial patterns of amyloid beta deposition in three mouse models of Alzheimer’s disease,” J. Comp. Neurol. 2018, 24555 (2018).
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B. Pinzer, M. Cacquevel, P. Modregger, S. McDonald, J. Bensadoun, T. Thuering, P. Aebischer, and M. Stampanoni, “Imaging brain amyloid deposition using grating-based differential phase contrast tomography,” Neuroimage 61, 1336–1346 (2012).
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Cantoni, M.

A. Kreshuk, C. N. Straehle, C. Sommer, U. Koethe, M. Cantoni, G. Knott, and F. A. Hamprecht, “Automated detection and segmentation of synaptic contacts in nearly isotropic serial electron microscopy images,” PloS one 6, e24899 (2011).
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Carbonell, F.

M. Grand’Maison, S. P. Zehntner, M.-K. Ho, F. Hébert, A. Wood, F. Carbonell, A. P. Zijdenbos, E. Hamel, and B. J. Bedell, “Early cortical thickness changes predict β-amyloid deposition in a mouse model of alzheimer’s disease,” Neurobiol. disease 54, 59–67 (2013).
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Cardona, A.

J. Schindelin, I. Arganda-Carreras, E. Frise, V. Kaynig, M. Longair, T. Pietzsch, S. Preibisch, C. Rueden, S. Saalfeld, B. Schmid, J.-Y. Tinevez, D. J. White, V. Hartenstein, K. Eliceiri, P. Tomancak, and A. Cardona, “Fiji: an open-source platform for biological-image analysis,” Nat. methods 9, 676 (2012).
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Carré, D.

M. E. Vandenberghe, A.-S. Hérard, N. Souedet, E. Sadouni, M. D. Santin, D. Briet, D. Carré, J. Schulz, P. Hantraye, P.-E. Chabrier, T. Rooney, T. Debeir, V. Blanchard, L. Pradier, M. Dhenain, and T. Delzescaux, “High-throughput 3d whole-brain quantitative histopathology in rodents,” Sci. reports 6, 20958 (2016).
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Cernadas, E.

M. Fernández-Delgado, E. Cernadas, S. Barro, and D. Amorim, “Do we need hundreds of classifiers to solve real world classification problems?” The J. Mach. Learn. Res. 15, 3133–3181 (2014).

Chabrier, P.-E.

M. E. Vandenberghe, A.-S. Hérard, N. Souedet, E. Sadouni, M. D. Santin, D. Briet, D. Carré, J. Schulz, P. Hantraye, P.-E. Chabrier, T. Rooney, T. Debeir, V. Blanchard, L. Pradier, M. Dhenain, and T. Delzescaux, “High-throughput 3d whole-brain quantitative histopathology in rodents,” Sci. reports 6, 20958 (2016).
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Chami, L.

F. Letronne, G. Laumet, A.-M. Ayral, J. Chapuis, F. Demiautte, M. Laga, M. E. Vandenberghe, N. Malmanche, F. Leroux, F. Eysert, Y. Sottejeau, L. Chami, A. Flaig, C. Bauer, P. Dourlen, M. Lesaffre, C. Delay, L. Huot, J. Dumont, E. Werkmeister, F. Lafont, T. Mendes, F. Hansmannel, B. Dermaut, B. Deprez, A.-S. Hérard, M. Dhenain, N. Souedet, F. Pasquier, D. Tulasne, C. Berr, J.-J. Hauw, Y. Lemoine, P. Amouyel, D. Mann, R. Déprez, F. Checler, D. Hot, T. Delzescaux, K. Gevaert, and J.-C. Lambert, “Adam30 downregulates app-linked defects through cathepsin d activation in alzheimer’s disease,” EBioMedicine 9, 278–292 (2016).
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A. Ebadi, J. L. Dalboni da Rocha, D. B. Nagaraju, F. Tovar-Moll, I. Bramati, G. Coutinho, R. Sitaram, and P. Rashidi, “Ensemble classification of alzheimer’s disease and mild cognitive impairment based on complex graph measures from diffusion tensor images,” Front. neuroscience 11, 56 (2017).
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Supplementary Material (1)

NameDescription
» Dataset 1       OPT datasets, Ilastik pipeline, and manual segmentation

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Figures (7)

Fig. 1
Fig. 1 Sample preparation and experimental setup. (A) Photographs of a mouse brain through the different steps of the preparation protocol. (B) Scheme of the optical projection tomography setup. AL, aspheric lens; RL, relay lens; EX, excitation filter; DC, dichroic mirror; M1/2, mirrors; OL, objective lens; EM, emission filter; DI, diaphragm; TL, tube lens; ro, rotation.
Fig. 2
Fig. 2 Processing pipeline. From OPT acquisition, the projections are used to reconstruct a 3D image with a filtered back-projection algorithm. After a normalization step, a random forest classifier identifies voxels corresponding to plaques in the image volume. Quantitative measurements can be extracted from the segmentation mask, and statistical analyses can be conducted.
Fig. 3
Fig. 3 Classification procedure with random forests. For the sake of simplicity, 2D pixels and a 2-class problem are depicted. (A) Training phase. A few voxels from each classes (here A, the object, and B, the background) are manually labelled as ground truth. A collection of measurements, referred to as features, are extracted to characterize these voxels. Here, only two (intensity and texture-based features) are shown. A forest of decision trees is then constructed. Each stage of a decision tree is built by randomly picking a feature and choosing a boundary value that best separates each class according to this measurement. Here, three trees are shown. (B) Classification, or prediction, phase. For each unlabeled voxel, the same set of features as in training phase are extracted. These measurements are then fed to the random forest trees, which provide class prediction. The final probability is retrieved as the percentage of trees predicting each class.
Fig. 4
Fig. 4 Imaging of amyloid pathology progression. (A–C) Renderings of OPT images from the three age-groups: young, middle-aged, and old, respectively. Blue arrows, cerebellum; White arrows, amyloid plaques in the subiculum; Blue arrowheads, blood vessels; White arrowheads, cortical barrel fields. (D) Rendering from the control group. (E–H) Corresponding renderings after random forest classification and thresholding. The amyloid plaques, in yellow (thresholded at 0.5), are overlaid with the brain anatomy, in grey (thresholded at 0.7), whose transparency is reduced for visualization purposes.
Fig. 5
Fig. 5 Visual comparison of standard thresholding with random forests. (A) Rendering of an OPT image from an old mouse brain. (B) Corresponding rendering after thresholding voxel intensities at 70 % of the bit depth. DIT fails to isolate plaque signal from other brain structures, and results in several faint intensity plaques to be missed. Blue arrowheads, blood vessels; Tan arrowheads, cerebellum; Purple arrowhead, undetermined artefact. (C) Corresponding rendering of random forest predictions thresholded at 0.5.
Fig. 6
Fig. 6 Performance evaluation of the random forest classifier. Comparison of ROC analysis for (A) brain voxels identification and (B) plaque voxels identification using the random forest predictions (RF, dashed line), and competing approaches such as direct intensity thresholding (DIT, solid line), Laplacian of Gaussian filtering (LoG, dash-dotted line), and convolutional neural networks predictions (CNN, dotted line).
Fig. 7
Fig. 7 Representation of the amyloidosis statistical analysis Boxplots of the quantitative measures of amyloid progression for each group of mice. *, p < .05 (Tuckey’s test); n.s., not statistically significant.

Tables (4)

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Table 1 List of samples processed. The * indicates samples, which were partially annotated for the training of the random forest classifiers. The indicates samples, which were used to generate Fig. 4. Tg/0, transgenic animals; +/+, wild type animals.

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Table 2 Performance evaluation of the random forest classifier. Comparison of the area under the curve (AUC) corresponding to the ROC curves presented in Fig. 6 for brain and plaque identification using the random forest predictions (RF), and competing approaches such as direct intensity thresholding (DIT), Laplacian of Gaussian filtering (LoG), and convolutional neural networks predictions (CNN).

Tables Icon

Table 3 Performance evaluation of the random forest classifier. Comparison of the accuracy, sensitivity, specificity and dice metrics for brain and plaque identification using the random forest predictions (RF), and competing approaches such as direct intensity thresholding (DIT), Laplacian of Gaussian filtering (LoG), and convolutional neural networks predictions (CNN).

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Table 4 Quantitative measurements of amyloid plaque quantity in all samples. The * indicates samples which were partially annotated for the training of the random forest classifiers. The indicates samples, which were used to generate Fig. 4. The mean and standard deviation (SD) is included for each measure and group, which are compared in this study. The control group serves as quality control.

Equations (1)

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Accuracy = TP + TN TP + TN + FP + FN , Sensitivity = TP TP + FN , Specificity = TP TN + FP , Dice = 2 TP 2 TP + FP + FN ,