Euler characteristic surfaces

Gabriele Beltramo; Primoz Skraba; Rayna Andreeva; Rik Sarkar; Ylenia Giarratano; Miguel O. Bernabeu

doi:10.3934/fods.2021027

Article Contents

2022, Volume 4, Issue 4: 505-536. Doi: 10.3934/fods.2021027

This issue Previous Article Intrinsic disease maps using persistent cohomology Next Article Reconstructing linearly embedded graphs: A first step to stratified space learning

Euler characteristic surfaces

1.
School of Mathematical Sciences, Queen Mary University of London, London, E1 4NS, UK
2.
School of Informatics, University of Edinburgh, Edinburgh, EH8 9AB, UK
3.
Centre for Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, EH16 4UX, UK

^* Corresponding author
^* Corresponding author

Received: February 2021

Revised: September 2021

Early access: November 2021

Published: December 2022

Abstract / Introduction Full Text(HTML) Figure(18) / Table(4) Related Papers Cited by

Abstract

In this paper, we investigate the use of the Euler characteristic for the topological data analysis, particularly over higher dimensional parameter spaces. The Euler characteristic is a classical, well-understood topological invariant that has appeared in numerous applications, primarily in the context of random fields. The goal of this paper, is to present the extension of using the Euler characteristic in higher dimensional parameter spaces. The topological data analysis of higher dimensional parameter spaces using stronger invariants such as homology, has and continues to be the subject of intense research. However, as important theoretical and computational obstacles remain, the use of the Euler characteristic represents an important intermediary step toward multi-parameter topological data analysis. We show the usefulness of the techniques using generated examples as well as a real world dataset of detecting diabetic retinopathy in retinal images.

Keywords:

Mathematics Subject Classification: Primary: 62R40, 68T09; Secondary: 55N31.

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Figure 1. From left to right: $ 3\times 3 $ gray-scale image, full cubical complex $ Q $ of the image in a), finite point set in $ \mathbb{R}^2 $, and Delaunay complex $ D $ of the points in (c)

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Figure 2. Filtrations of the example data in Figure 1, for two given set of values $ \{ a_s \}_{s = 1}^m $. In both (a) and (b) the filtration parameters $ a_s $ are displayed above each subcomplex. The last subcomplexes in the sequence are the full cubical complex $ Q $ and the Delaunay complex $ D $

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Figure 3. The plot in (a) is the Euler characteristic curve of the image in Figure 1a, where sublevel sets are taken at all values between $ 0 $ and $ 255 $. The plot in (b) is the Euler characteristic curve of the points in Figure 1c, where the sublevel sets are taken at values $ a_s $ such that there is a one simplex difference between $ Q_{a_{i-1}} $ and $ Q_{a_s} $ for each $ i \in [1, m] $

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Figure 4. Contour plot of $ S_{(h_{M_1}, h_{M_2})} $ of a pair of random images $ M_1 $, $ M_2 $ with correlation coefficient equal to $ 0.1 $ in (a) and equal to $ 0.8 $ in (b). The difference between these two Euler surfaces is the Euler terrain in (c)

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Figure 5. The realtionship between the ECT and the ECS for functions on $ \mathbb{R}^2 $. For every direction $ \alpha $, the ECT computes the Euler characteristic curve based on the filtration arising from the height function in that direction. In the standard setup, it would be a direction on the sphere $ \mathbb{S}^2 $, but it could equivalently be all "directions'' in $ \mathbb{S}^1\times \mathbb{R} $. The 2-dimensional Euler surface we consider is a direction and the function sublevel sets, so the Euler surface can be thought of as representing the slice shown

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Figure 6. Euler characteristic changes produced by adding an elementary cube of maximal dimension in a two-dimensional cubical complex $ Q_{s-1, t} $. In (a) the change is equal to $ -3 $, while in (b) it is $ +1 $

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Figure 7. The images in (a) are eight of the twenty-four patterns in the $ \texttt{OUTEX_TC_00000} $ test suite. The images in (b) are a random selection of the $ 70,000 $ handwritten digits from the MNIST database

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Figure 8. Points sampled from two Clayton copula distributions. In (a) $ \theta = 1 $, while in (b) $ \theta = 5 $

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Figure 9. The average Euler characteristic surfaces of random images generated with random points as in Figure 8. Each point represents the function values of a voxel (in a 3D image) generated from Clayton copula distributions to have uniform marginal distributions but different joint distributions. In (a), the surface was obtained with $ \theta = 1 $, and in (b) with $ \theta = 5 $. The absolute value of the difference of the surfaces in (a) and (b) is in (c)

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Figure 10. Point processes, corresponding to a Poisson point process in (a) and Hawkes cluster process in (b)

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Figure 11. Average Euler characteristic surfaces obtained from the point processes in Figure 10. In (a) and (b) the average surfaces of points obtained from a Poisson and Hawkes cluster process respectively. In (c) the Euler terrain representing their difference. In (d) the black area represent regions of the parameter space where the two average surfaces in (a) and (b) are significantly different

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Figure 12. Examples of the complex built at the radius and density corresponding to Region A in Figure 11(c) for a cluster process (a, b) and the Poisson process (c, d)

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Figure 13. OCTA scans from Control, DR and NoDR patients. Changes to the microvasculature are apparent with disease progression. For example, the vessel density reduces and the foveal avascular zone (FAZ), which is the black regions in the middles of the image, is enlarged and distorted with less circular shape

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Figure 14. Example of the ECS with $ M_2 $ being the complement image

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Figure 15. Example of the ECS with $ M_2 $ being the radial gradient image

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Figure 16. Examples of regions of interest in the terrains of OCTAGON

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Figure 17. Examples of level-sets of images of the control (a, b) and DR (c, d) corresponding to Region B

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Figure 18. Examples of sublevel sets with the radial mask shown in yellow with the control shown in (a, b) and DR (c, d) corresponding to Region A

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Table 1. Classification results obtained with Euler characteristic based feature vectors and logistic regression

$ \texttt{Outex_TC_00000} $
Features	Avg test accuracy
Euler curve - pixel intensity	$ 91.08\pm 1.57 $ %
Euler surface - pixel intensity, Laplacian	$ \mathbf{96.29}\pm 1.24 $ %

MNIST
Features	Test accuracy
Euler curve - pixel intensity	$ 33.63 $ %
Euler surface - pixel intensity, top/bottom gradient	$ \mathbf{71.79} $ %

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Table 2. Tables of classification performances in the Control vs. DR study

NHS Lothian, Control vs. DR
	Baseline	Our approach	VGG16
Overall Acc	$0.72 \pm 0.03$	$0.81 \pm 0.04$	0.84 $\pm$ 0.07
Sen (Control)	0.94 $\pm$ 0.05	0.94 $\pm$ 0.05	$0.88 \pm 0.07$
Spe (Control)	$0.30 \pm 0.06$	$0.60 \pm 0.06$	0.77 $\pm$ 0.09
AUC	$0.75 \pm 0.06$	0.88 $\pm$ 0.03	0.88 $\pm$ 0.12

OCTAGON, Control vs. DR
	Baseline	Our approach	VGG16
Overall Acc	$0.82 \pm 0.04$	0.87 $\pm$ 0.04	$0.84 \pm 0.07$
Sen (Control)	$0.87 \pm 0.04$	0.96 $\pm$ 0.04	1.00 $\pm$ 0.00
Spe (Control)	0.71 $\pm$ 0.08	0.71 $\pm$ 0.08	$0.53 \pm 0.20$
AUC	$0.87 \pm 0.04$	0.91 $\pm$ 0.03	0.94 $\pm$ 0.06

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Table 3. Table of classification performances with transfer learning

	Controls		NoDR		DR
	Our approach	VGG16	Our approach	VGG16	Our approach	VGG16
ACC	0.68 $ \pm $ 0.05	0.78 $ \pm $ 0.05	0.81 $ \pm $ 0.04	$ 0.72 \pm 0.04 $	0.76 $ \pm $ 0.02	0.77 $ \pm $ 0.04
SEN	$ 0.79 \pm 0.05 $	0.90 $ \pm $ 0.05	0.56 $ \pm $ 0.06	$ 0.20 \pm 0.13 $	0.26 $ \pm $ 0.14	0.55 $ \pm $ 0.11
SPE	0.57 $ \pm $ 0.09	0.67 $ \pm $ 0.11	0.90 $ \pm $ 0.04	0.88$ \pm $ 0.05	0.90 $ \pm $ 0.02	$ 0.86 \pm 0.05 $
AUC	0.80 $ \pm $ 0.04	0.90 $ \pm $ 0.15	0.70 $ \pm $ 0.04	0.67$ \pm $ 0.28	0.86 $ \pm $ 0.06	$ 0.75 \pm 0.22 $

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Table 4. Correlation results for NHS Lothian and OCTAGON

	EC ($ p $-value), NHS Lothian	EC ($ p $-value), OCTAGON
(VD, EC(VD))	$ 0.86 (1.65 \times 10^{-12}) $	$ 0.20 (0.20) $
(FAZ, EC(FAZ))	$ 0.55 (2.52 \times 10^{-4}) $	$ 0.57 (7.13 \times 10^{-5}) $

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References

[1]	R. J. Adler, E. Subag and J. E. Taylor, et al., Rotation and scale space random fields and the gaussian kinematic formula, Ann. Statist., 40 (2012), 2910-2942. doi: 10.1214/12-AOS1055.
[2]	R. J. Adler and J. E. Taylor, Random Fields and Geometry, Springer, New York, 2007.
[3]	M. N. Alam, T. Son, D. Toslak, J. I. Lim and X. Yao, Quantitative artery-vein analysis in optical coherence tomography angiography of diabetic retinopathy, in Ophthalmic Technologies XXIX, vol. 10858, International Society for Optics and Photonics, (2019), 1085802. doi: 10.1117/12.2510213.
[4]	M. Alam, Y. Zhang, J. I. Lim, R. V. Chan, M. Yang and X. Yao, Quantitative optical coherence tomography angiography features for objective classification and staging of diabetic retinopathy, Retina, 40 (2020), 322-332. doi: 10.1097/IAE.0000000000002373.
[5]	R. Andreeva, A. Fontanella, Y. Giarratano and M. O. Bernabeu, Dr detection using optical coherence tomography angiography (octa): A transfer learning approach with robustness analysis, in International Workshop on Ophthalmic Medical Image Analysis, Springer, (2020), 11–20.
[6]	Y. Baryshnikov and R. Ghrist, Target enumeration via Euler characteristic integrals, SIAM J. Appl. Math., 70 (2009), 825-844. doi: 10.1137/070687293.
[7]	Y. Baryshnikov and R. Ghrist, Euler integration over definable functions, Proc. Natl. Acad. Sci. USA, 107 (2010), 9525-9530. doi: 10.1073/pnas.0910927107.
[8]	Y. Baryshnikov, R. Ghrist and D. Lipsky, Inversion of Euler integral transforms with applications to sensor data, Inverse Problems, 27 (2011), 124001, 10 pp. doi: 10.1088/0266-5611/27/12/124001.
[9]	O. Bobrowski and P. Skraba, Homological percolation and the Euler characteristic, Phys. Rev. E, 101 (2020), 032304, 16 pp. doi: 10.1103/physreve.101.032304.
[10]	F. Cagliari and C. Landi, Finiteness of rank invariants of multidimensional persistent homology groups, Appl. Math. Lett., 24 (2011), 516-518. doi: 10.1016/j.aml.2010.11.004.
[11]	G. Carlsson and A. Zomorodian, The theory of multidimensional persistence, Discrete Comput. Geom., 42 (2009), 71-93. doi: 10.1007/s00454-009-9176-0.
[12]	F. Chazal, L. J. Guibas, S. Y. Oudot and P. Skraba, Persistence-based clustering in Riemannian manifolds, J. ACM, 60 (2013), Art. 41, 38 pp. doi: 10.1145/2535927.
[13]	L. Crawford, A. Monod, A. X. Chen, S. Mukherjee and R. Rabadán, Predicting clinical outcomes in glioblastoma: an application of topological and functional data analysis, J. Amer. Statist. Assoc., 115 (2020), 1139-1150. doi: 10.1080/01621459.2019.1671198.
[14]	J. Curry, R. Ghrist and M. Robinson, Euler calculus with applications to signals and sensing, in Proc. Sympos. Appl. Math., vol. 70, (2012), 75–146. doi: 10.1090/psapm/070/589.
[15]	J. Curry, S. Mukherjee and K. Turner, How many directions determine a shape and other sufficiency results for two topological transforms, arXiv preprint, arXiv: 1805.09782.
[16]	M. Díaz, J. Novo, P. Cutrín, F. Gómez-Ulla, M. G. Penedo and M. Ortega, Automatic segmentation of the foveal avascular zone in ophthalmological OCT-A images, PloS One, 14.
[17]	P. J. Diggle, Statistical Analysis of Spatial and Spatio-Temporal Point Patterns, CRC press, 2014.
[18]	H. Edelsbrunner and J. L. Harer, Computational Topology: An Introduction, American Mathematical Society, 2010. doi: 10.1090/mbk/069.
[19]	H. Edelsbrunner and E. P. Mücke, Three-dimensional alpha shapes, VVS '92: Proceedings of the 1992 workshop on Volume visualization, (1992), 75–82. doi: 10.1145/147130.147153.
[20]	J. M. Ekoé, M. Rewers, R. Williams and P. Zimmet, The Epidemiology of Diabetes Mellitus, John Wiley & Sons, 2008.
[21]	B. T. Fasy, S. Micka, D. L. Millman, A. Schenfisch and L. Williams, Challenges in reconstructing shapes from euler characteristic curves, arXiv preprint, arXiv: 1811.11337.
[22]	F. J. Freiberg, M. Pfau, J. Wons, M. A. Wirth, M. D. Becker and S. Michels, Optical coherence tomography angiography of the foveal avascular zone in diabetic retinopathy, Graefe's Archive for Clinical and Experimental Ophthalmology, 254 (2016), 1051-1058. doi: 10.1007/s00417-015-3148-2.
[23]	R. Ghrist, R. Levanger and H. Mai, Persistent homology and Euler integral transforms, J. Appl. Comput. Topol., 2 (2018), 55-60. doi: 10.1007/s41468-018-0017-1.
[24]	R. Ghrist and M. Robinson, Euler–Bessel and Euler–Fourier transforms, Inverse Problems, 27 (2011), 124006, 12 pp. doi: 10.1088/0266-5611/27/12/124006.
[25]	Y. Giarratano, E. Bianchi, C. Gray, A. Morris, T. MacGillivray, B. Dhillon and M. O. Bernabeu, Automated segmentation of optical coherence tomography angiography images: Benchmark data and clinically relevant metrics, Translational Vision Science & Technology, 9 (2020), 5-5.
[26]	Y. Giarratano, A. Pavel, J. Lian, R. Andreeva, A. Fontanella, R. Sarkar, L. Reid, S. Forbes, D. Pugh, T. E. Farrah, N. Dhaun, B. Dhillon, T. MacGillivray and M. O. Bernabeu, A framework for the discovery of retinal biomarkers in Optical Coherence Tomography Angiography (OCTA), MICCAI Workshop on Ophthalmic Medical Image Analysis – OMIA 2020.
[27]	Y. Giarratano, A. Pavel, J. Lian, R. Andreeva, A. Fontanella, R. Sarkar, L. J. Reid, S. Forbes, D. Pugh, T. E. Farrah et al., A framework for the discovery of retinal biomarkers in Optical Coherence Tomography Angiography (OCTA), in International Workshop on Ophthalmic Medical Image Analysis, Springer, (2020), 155–164.
[28]	V. Guillemin and A. Pollack, Differential Topology, vol. 370, American Mathematical Soc., 2010. doi: 10.1090/chel/370.
[29]	H. A. Harrington, N. Otter, H. Schenck and U. Tillmann, Stratifying multiparameter persistent homology, SIAM J. Appl. Algebra Geom., 3 (2019), 439-471. doi: 10.1137/18M1224350.
[30]	A. Hatcher, Algebraic Topology, Cambridge University Press, Cambridge, 2002.
[31]	T. Heiss and H. Wagner, Streaming algorithm for Euler characteristic curves of multidimensional images, CAIP 2017: Computer Analysis of Images and Patterns, 10424 (2017), 397-409. doi: 10.1007/978-3-319-64689-3_32.
[32]	M. Hofert, I. Kojadinovic, M. Mächler and J. Yan, Elements of Copula Modeling with R, Springer, 2018. doi: 10.1007/978-3-319-89635-9.
[33]	Y. Jia, O. Tan, J. Tokayer, B. Potsaid, Y. Wang, J. J. Liu, M. F. Kraus, H. Subhash, J. G. Fujimoto and J. Hornegger, et al., Split-spectrum amplitude-decorrelation angiography with optical coherence tomography, Optics Express, 20 (2012), 4710-4725. doi: 10.1364/OE.20.004710.
[34]	M. Kahle, Topology of random clique complexes, Discrete Math., 309 (2009), 1658-1671. doi: 10.1016/j.disc.2008.02.037.
[35]	M. Kahle, Topology of random simplicial complexes: A survey, Algebraic Topology: Applications and New Directions, 620 (2014), 201-221. doi: 10.1090/conm/620/12367.
[36]	M. Kashiwara and P. Schapira, Integral transforms with exponential kernels and laplace transform, J. Amer. Math. Soc., 10 (1997), 939-972. doi: 10.1090/S0894-0347-97-00245-2.
[37]	M. Kashiwara and P. Schapira, Persistent homology and microlocal sheaf theory, J. Appl. Comput. Topol., 2 (2018), 83-113. doi: 10.1007/s41468-018-0019-z.
[38]	J. Khadamy, K. A. Aghdam and K. G. Falavarjani, An update on optical coherence tomography angiography in diabetic retinopathy, Journal of Ophthalmic & Vision Research, 13 (2018), 487.
[39]	D. P. Kroese and Z. I. Botev, Spatial process generation, arXiv preprint, arXiv: 1308.0399.
[40]	D. Le, M. Alam, B. A. Miao, J. I. Lim and X. Yao, Fully automated geometric feature analysis in optical coherence tomography angiography for objective classification of diabetic retinopathy, Biomedical Optics Express, 10 (2019), 2493-2503. doi: 10.1364/BOE.10.002493.
[41]	Y. Lecun, L. Bottou, Y. Bengio and P. Haffner, Gradient-based learning applied to document recognition, Proceedings of the IEEE, 86 (1998), 2278-2324. doi: 10.1109/5.726791.
[42]	T. Leinster, The Euler characteristic of a category, Doc. Math., 13 (2008), 21-49.
[43]	M. P. Lesnick, Multidimensional Interleavings and Applications to Topological Inference, Stanford University, 2012.
[44]	X.-X. Li, W. Wu, H. Zhou, J.-J. Deng, M.-Y. Zhao, T.-W. Qian, C. Yan, X. Xu and S.-Q. Yu, A quantitative comparison of five optical coherence tomography angiography systems in clinical performance, International Journal of Ophthalmology, 11 (2018), 1784.
[45]	N. Linial and Y. Peled, On the phase transition in random simplicial complexes, Ann. of Math., 184 (2016), 745-773. doi: 10.4007/annals.2016.184.3.3.
[46]	A. McCleary and A. Patel, Multiparameter persistence diagrams, arXiv preprint.
[47]	National Health Service, Diabetic retinopathy, Available from: https://www.nhs.uk/conditions/diabetic-retinopathy, 2020, [Accessed on 1 August 2020].
[48]	R. B. Nelsen, An Introduction to Copulas, Second edition. Springer Series in Statistics. Springer, New York, 2006.
[49]	T. Ojala, T. Mäenpää, M. Pietikäinen, J. Viertola, J. Kyllönen and S. Huovinen, Outex-new framework for empirical evaluation of texture analysis algorithms, in Proceedings of the 16th International Conference on Pattern Recognition, (2002), 701–706. doi: 10.1109/ICPR.2002.1044854.
[50]	S. Oudot and E. Solomon, Inverse problems in topological persistence, in Topological Data Analysis, Springer, (2020), 405–433.
[51]	F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss and V. Dubourg, et al., Scikit-learn: Machine learning in Python, J. Mach. Learn. Res., 12 (2011), 2825-2830.
[52]	W. D. Penny, K. J. Friston, J. T. Ashburner, S. J. Kiebel and T. E. Nichols, Statistical Parametric Mapping: The Analysis of Functional Brain Images, Elsevier, 2011.
[53]	P. Pranav, R. Van de Weygaert, G. Vegter, B. J. Jones, R. J. Adler, J. Feldbrugge, C. Park, T. Buchert and M. Kerber, Topology and geometry of Gaussian random fields I: On Betti numbers, Euler characteristic, and Minkowski functionals, Monthly Notices of the Royal Astronomical Society, 485 (2019), 4167-4208. doi: 10.1093/mnras/stz541.
[54]	E. Richardson and M. Werman, Efficient classification using Euler characteristic, Pattern Recognition Letters, 49 (2014), 99-106. doi: 10.1016/j.patrec.2014.07.001.
[55]	E. Richardson and M. Werman, Efficient classification using the Euler characteristic, Pattern Recognition Letters, 49 (2014), 99-106. doi: 10.1016/j.patrec.2014.07.001.
[56]	H. S. Sandhu, N. Eladawi, M. Elmogy, R. Keynton, O. Helmy, S. Schaal and A. El-Baz, Automated diabetic retinopathy detection using optical coherence tomography angiography: A pilot study, British Journal of Ophthalmology, 102 (2018), 1564-1569.
[57]	M. Sasongko, T. Wong, T. Nguyen, C. Cheung, J. Shaw and J. Wang, Retinal vascular tortuosity in persons with diabetes and diabetic retinopathy, Diabetologia, 54 (2011), 2409-2416. doi: 10.1007/s00125-011-2200-y.
[58]	P. Schapira, Tomography of constructible functions, in International Symposium on Applied Algebra, Algebraic Algorithms, and Error-Correcting Codes, Springer, (1995), 427–435. doi: 10.1007/3-540-60114-7_33.
[59]	M. Scolamiero, W. Chachólski, A. Lundman, R. Ramanujam and S. Öberg, Multidimensional persistence and noise, Found. Comput. Math., 17 (2017), 1367-1406. doi: 10.1007/s10208-016-9323-y.
[60]	S. Stolte and R. Fang, A survey on medical image analysis in diabetic retinopathy, Medical Image Analysis, 64 (2020), 101742. doi: 10.1016/j.media.2020.101742.
[61]	N. Takase, M. Nozaki, A. Kato, H. Ozeki, M. Yoshida and Y. Ogura, Enlargement of foveal avascular zone in diabetic eyes evaluated by en face optical coherence tomography angiography, Retina, 35 (2015), 2377-2383. doi: 10.1097/IAE.0000000000000849.
[62]	K. Y. Tey, K. Teo, A. C. Tan, K. Devarajan, B. Tan, J. Tan, L. Schmetterer and M. Ang, Optical coherence tomography angiography in diabetic retinopathy: A review of current applications, Eye and Vision, 6 (2019), 1-10. doi: 10.1186/s40662-019-0160-3.
[63]	I. A. Thompson, A. K. Durrani and S. Patel, Optical coherence tomography angiography characteristics in diabetic patients without clinical diabetic retinopathy, Eye, 33 (2019), 648-652. doi: 10.1038/s41433-018-0286-x.
[64]	K. Turner, S. Mukherjee and D. M. Boyer, Persistent homology transform for modeling shapes and surfaces, Inf. Inference, 3 (2014), 310-344. doi: 10.1093/imaiai/iau011.
[65]	R. Van De Weygaert, G. Vegter, H. Edelsbrunner, B. J. Jones, P. Pranav, C. Park, W. A. Hellwing, B. Eldering, N. Kruithof, E. P. Bos et al., Alpha, Betti and the Megaparsec Universe: On the topology of the cosmic web, in Transactions on Computational Science XIV, Springer, (2011), 60–101. doi: 10.1007/978-3-642-25249-5_3.
[66]	L. van den Dries, Tame Topology and O-Minimal Structures, vol. 248, Cambridge university press, 1998. doi: 10.1017/CBO9780511525919.
[67]	K. J. Worsley, Detecting activation in fMRI data, Stat. Methods Med. Res., 12 (2003), 401-418. doi: 10.1191/0962280203sm340ra.
[68]	K. J. Worsley, J. E. Taylor, F. Tomaiuolo and J. Lerch, Unified univariate and multivariate random field theory, Neuroimage, 23 (2004), S189–S195. doi: 10.1016/j.neuroimage.2004.07.026.
[69]	X. Yao, M. N. Alam, D. Le and D. Toslak, Quantitative optical coherence tomography angiography: A review, Experimental Biology and Medicine, 245 (2020), 301-312. doi: 10.1177/1535370219899893.