Abstract

Gestational age estimation at time of birth is critical for determining the degree of prematurity of the infant and for administering appropriate postnatal treatment. We present a fully automated algorithm for estimating gestational age of premature infants through smartphone lens imaging of the anterior lens capsule vasculature (ALCV). Our algorithm uses a fully convolutional network and blind image quality analyzers to segment usable anterior capsule regions. Then, it extracts ALCV features using a residual neural network architecture and trains on these features using a support vector machine-based classifier. The classification algorithm is validated using leave-one-out cross-validation on videos captured from 124 neonates. The algorithm is expected to be an influential tool for remote and point-of-care gestational age estimation of premature neonates in low-income countries. To this end, we have made the software open source.

© 2018 Optical Society of America under the terms of the OSA Open Access Publishing Agreement

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References

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2018 (3)

2017 (8)

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

F. G. Venhuizen, B. van Ginneken, B. Liefers, M. J. J. P. van Grinsven, S. Fauser, C. Hoyng, T. Theelen, and C. I. Sánchez, “Robust total retina thickness segmentation in optical coherence tomography images using convolutional neural networks,” Biomed. Opt. Express 8(7), 3292–3316 (2017).
[Crossref] [PubMed]

S. Xiao, F. Bucher, Y. Wu, A. Rokem, C. S. Lee, K. V. Marra, R. Fallon, S. Diaz-Aguilar, E. Aguilar, M. Friedlander, and A. Y. Lee, “Fully automated, deep learning segmentation of oxygen-induced retinopathy images,” JCI Insight 2(24), e97585 (2017).
[Crossref] [PubMed]

D. Cunefare, L. Fang, R. F. Cooper, A. Dubra, J. Carroll, and S. Farsiu, “Open source software for automatic detection of cone photoreceptors in adaptive optics ophthalmoscopy using convolutional neural networks,” Sci. Rep. 7(1), 6620 (2017).
[Crossref] [PubMed]

L. Fang, D. Cunefare, C. Wang, R. H. Guymer, S. Li, and S. Farsiu, “Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search,” Biomed. Opt. Express 8(5), 2732–2744 (2017).
[Crossref] [PubMed]

R. Gargeya and T. Leng, “Automated identification of diabetic retinopathy using deep learning,” Ophthalmology 124(7), 962–969 (2017).
[Crossref] [PubMed]

D. W. Skupski, J. Owen, S. Kim, K. M. Fuchs, P. S. Albert, and K. L. Grantz, “Estimating Gestational Age From Ultrasound Fetal Biometrics,” Obstet. Gynecol. 130(2), 433–441 (2017).
[Crossref] [PubMed]

A. C. Lee, P. Panchal, L. Folger, H. Whelan, R. Whelan, B. Rosner, H. Blencowe, and J. E. Lawn, “Diagnostic Accuracy of Neonatal Assessment for Gestational Age Determination: A Systematic Review,” Pediatrics 140(6), e20171423 (2017).
[Crossref] [PubMed]

2016 (4)

J. P. Campbell, E. Ataer-Cansizoglu, V. Bolon-Canedo, A. Bozkurt, D. Erdogmus, J. Kalpathy-Cramer, S. N. Patel, J. D. Reynolds, J. Horowitz, K. Hutcheson, M. Shapiro, M. X. Repka, P. Ferrone, K. Drenser, M. A. Martinez-Castellanos, S. Ostmo, K. Jonas, R. V. Chan, and M. F. Chiang, “Expert diagnosis of plus disease in retinopathy of prematurity from computer-based image analysis,” JAMA Ophthalmol. 134(6), 651–657 (2016).
[Crossref] [PubMed]

V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, J. Cuadros, R. Kim, R. Raman, P. C. Nelson, J. L. Mega, and D. R. Webster, “Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs,” JAMA 316(22), 2402–2410 (2016).
[Crossref] [PubMed]

M. J. van Grinsven, T. Theelen, L. Witkamp, J. van der Heijden, J. P. van de Ven, C. B. Hoyng, B. van Ginneken, and C. I. Sánchez, “Automatic differentiation of color fundus images containing drusen or exudates using a contextual spatial pyramid approach,” Biomed. Opt. Express 7(3), 709–725 (2016).
[Crossref] [PubMed]

R. Asaoka, H. Murata, A. Iwase, and M. Araie, “Detecting preperimetric glaucoma with standard automated perimetry using a deep learning classifier,” Ophthalmology 123(9), 1974–1980 (2016).
[Crossref] [PubMed]

2015 (1)

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521(7553), 436–444 (2015).
[Crossref] [PubMed]

2014 (2)

2013 (2)

A. Mittal, R. Soundararajan, and A. C. Bovik, “Making a “Completely Blind” Image Quality Analyzer,” IEEE Signal Process. Lett. 20(3), 209–212 (2013).
[Crossref]

H. Blencowe, S. Cousens, D. Chou, M. Oestergaard, L. Say, A.-B. Moller, M. Kinney, and J. Lawn, “Born too soon: the global epidemiology of 15 million preterm births,” Reprod. Health 10(Suppl 1), S2 (2013).
[Crossref] [PubMed]

2012 (1)

2011 (2)

R. Estrada, C. Tomasi, M. T. Cabrera, D. K. Wallace, S. F. Freedman, and S. Farsiu, “Enhanced video indirect ophthalmoscopy (VIO) via robust mosaicing,” Biomed. Opt. Express 2(10), 2871–2887 (2011).
[Crossref] [PubMed]

C.-C. Chang and C.-J. Lin, “LIBSVM: a library for support vector machines,” ACM transactions on intelligent systems and technology (TIST) 2, 27 (2011).

2007 (1)

2002 (1)

Z. Wang and A. C. Bovik, “A universal image quality index,” IEEE Signal Process. Lett. 9(3), 81–84 (2002).
[Crossref]

2000 (1)

G. Bradski and A. Kaehler, “OpenCV,” Dr. Dobbs J. Softw. Tools Prof. Program. 3, 2000 (2000).

1986 (1)

J. Canny, “A computational approach to edge detection,” IEEE Trans. Pattern Anal. Mach. Intell. 8(6), 679–698 (1986).
[Crossref] [PubMed]

1979 (1)

J. L. Ballard, K. K. Novak, and M. Driver, “A simplified score for assessment of fetal maturation of newly born infants,” J. Pediatr. 95(5), 769–774 (1979).
[Crossref] [PubMed]

1977 (1)

H. M. Hittner, N. J. Hirsch, and A. J. Rudolph, “Assessment of gestational age by examination of the anterior vascular capsule of the lens,” J. Pediatr. 91(3), 455–458 (1977).
[Crossref] [PubMed]

1970 (1)

L. M. Dubowitz, V. Dubowitz, and C. Goldberg, “Clinical assessment of gestational age in the newborn infant,” J. Pediatr. 77(1), 1–10 (1970).
[Crossref] [PubMed]

1945 (1)

L. R. Dice, “Measures of the amount of ecologic association between species,” Ecology 26(3), 297–302 (1945).
[Crossref]

Aguilar, E.

S. Xiao, F. Bucher, Y. Wu, A. Rokem, C. S. Lee, K. V. Marra, R. Fallon, S. Diaz-Aguilar, E. Aguilar, M. Friedlander, and A. Y. Lee, “Fully automated, deep learning segmentation of oxygen-induced retinopathy images,” JCI Insight 2(24), e97585 (2017).
[Crossref] [PubMed]

Albert, P. S.

D. W. Skupski, J. Owen, S. Kim, K. M. Fuchs, P. S. Albert, and K. L. Grantz, “Estimating Gestational Age From Ultrasound Fetal Biometrics,” Obstet. Gynecol. 130(2), 433–441 (2017).
[Crossref] [PubMed]

Araie, M.

R. Asaoka, H. Murata, A. Iwase, and M. Araie, “Detecting preperimetric glaucoma with standard automated perimetry using a deep learning classifier,” Ophthalmology 123(9), 1974–1980 (2016).
[Crossref] [PubMed]

Asaoka, R.

R. Asaoka, H. Murata, A. Iwase, and M. Araie, “Detecting preperimetric glaucoma with standard automated perimetry using a deep learning classifier,” Ophthalmology 123(9), 1974–1980 (2016).
[Crossref] [PubMed]

Ataer-Cansizoglu, E.

J. P. Campbell, E. Ataer-Cansizoglu, V. Bolon-Canedo, A. Bozkurt, D. Erdogmus, J. Kalpathy-Cramer, S. N. Patel, J. D. Reynolds, J. Horowitz, K. Hutcheson, M. Shapiro, M. X. Repka, P. Ferrone, K. Drenser, M. A. Martinez-Castellanos, S. Ostmo, K. Jonas, R. V. Chan, and M. F. Chiang, “Expert diagnosis of plus disease in retinopathy of prematurity from computer-based image analysis,” JAMA Ophthalmol. 134(6), 651–657 (2016).
[Crossref] [PubMed]

Ballard, J. L.

J. L. Ballard, K. K. Novak, and M. Driver, “A simplified score for assessment of fetal maturation of newly born infants,” J. Pediatr. 95(5), 769–774 (1979).
[Crossref] [PubMed]

Bengio, Y.

Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” Nature 521(7553), 436–444 (2015).
[Crossref] [PubMed]

X. Glorot and Y. Bengio, “Understanding the difficulty of training deep feedforward neural networks,” in Proceedings of the thirteenth international conference on artificial intelligence and statistics, 2010), 249–256.

Blau, S.

Blencowe, H.

A. C. Lee, P. Panchal, L. Folger, H. Whelan, R. Whelan, B. Rosner, H. Blencowe, and J. E. Lawn, “Diagnostic Accuracy of Neonatal Assessment for Gestational Age Determination: A Systematic Review,” Pediatrics 140(6), e20171423 (2017).
[Crossref] [PubMed]

H. Blencowe, S. Cousens, D. Chou, M. Oestergaard, L. Say, A.-B. Moller, M. Kinney, and J. Lawn, “Born too soon: the global epidemiology of 15 million preterm births,” Reprod. Health 10(Suppl 1), S2 (2013).
[Crossref] [PubMed]

Bogunovic, H.

T. Schlegl, S. M. Waldstein, H. Bogunovic, F. Endstraßer, A. Sadeghipour, A.-M. Philip, D. Podkowinski, B. S. Gerendas, G. Langs, and U. Schmidt-Erfurth, “Fully automated detection and quantification of macular fluid in OCT using deep learning,” Ophthalmology 125(4), 549–558 (2018).
[Crossref] [PubMed]

Bolon-Canedo, V.

J. P. Campbell, E. Ataer-Cansizoglu, V. Bolon-Canedo, A. Bozkurt, D. Erdogmus, J. Kalpathy-Cramer, S. N. Patel, J. D. Reynolds, J. Horowitz, K. Hutcheson, M. Shapiro, M. X. Repka, P. Ferrone, K. Drenser, M. A. Martinez-Castellanos, S. Ostmo, K. Jonas, R. V. Chan, and M. F. Chiang, “Expert diagnosis of plus disease in retinopathy of prematurity from computer-based image analysis,” JAMA Ophthalmol. 134(6), 651–657 (2016).
[Crossref] [PubMed]

Bovik, A. C.

A. Mittal, R. Soundararajan, and A. C. Bovik, “Making a “Completely Blind” Image Quality Analyzer,” IEEE Signal Process. Lett. 20(3), 209–212 (2013).
[Crossref]

Z. Wang and A. C. Bovik, “A universal image quality index,” IEEE Signal Process. Lett. 9(3), 81–84 (2002).
[Crossref]

Bozkurt, A.

J. P. Campbell, E. Ataer-Cansizoglu, V. Bolon-Canedo, A. Bozkurt, D. Erdogmus, J. Kalpathy-Cramer, S. N. Patel, J. D. Reynolds, J. Horowitz, K. Hutcheson, M. Shapiro, M. X. Repka, P. Ferrone, K. Drenser, M. A. Martinez-Castellanos, S. Ostmo, K. Jonas, R. V. Chan, and M. F. Chiang, “Expert diagnosis of plus disease in retinopathy of prematurity from computer-based image analysis,” JAMA Ophthalmol. 134(6), 651–657 (2016).
[Crossref] [PubMed]

Bradski, G.

G. Bradski and A. Kaehler, “OpenCV,” Dr. Dobbs J. Softw. Tools Prof. Program. 3, 2000 (2000).

Bucher, F.

S. Xiao, F. Bucher, Y. Wu, A. Rokem, C. S. Lee, K. V. Marra, R. Fallon, S. Diaz-Aguilar, E. Aguilar, M. Friedlander, and A. Y. Lee, “Fully automated, deep learning segmentation of oxygen-induced retinopathy images,” JCI Insight 2(24), e97585 (2017).
[Crossref] [PubMed]

Cabrera, M. T.

Campbell, J. P.

J. P. Campbell, E. Ataer-Cansizoglu, V. Bolon-Canedo, A. Bozkurt, D. Erdogmus, J. Kalpathy-Cramer, S. N. Patel, J. D. Reynolds, J. Horowitz, K. Hutcheson, M. Shapiro, M. X. Repka, P. Ferrone, K. Drenser, M. A. Martinez-Castellanos, S. Ostmo, K. Jonas, R. V. Chan, and M. F. Chiang, “Expert diagnosis of plus disease in retinopathy of prematurity from computer-based image analysis,” JAMA Ophthalmol. 134(6), 651–657 (2016).
[Crossref] [PubMed]

Canny, J.

J. Canny, “A computational approach to edge detection,” IEEE Trans. Pattern Anal. Mach. Intell. 8(6), 679–698 (1986).
[Crossref] [PubMed]

Carroll, J.

D. Cunefare, C. S. Langlo, E. J. Patterson, S. Blau, A. Dubra, J. Carroll, and S. Farsiu, “Deep learning based detection of cone photoreceptors with multimodal adaptive optics scanning light ophthalmoscope images of achromatopsia,” Biomed. Opt. Express 9(8), 3740–3756 (2018).
[Crossref] [PubMed]

D. Cunefare, L. Fang, R. F. Cooper, A. Dubra, J. Carroll, and S. Farsiu, “Open source software for automatic detection of cone photoreceptors in adaptive optics ophthalmoscopy using convolutional neural networks,” Sci. Rep. 7(1), 6620 (2017).
[Crossref] [PubMed]

Chan, R. V.

J. P. Campbell, E. Ataer-Cansizoglu, V. Bolon-Canedo, A. Bozkurt, D. Erdogmus, J. Kalpathy-Cramer, S. N. Patel, J. D. Reynolds, J. Horowitz, K. Hutcheson, M. Shapiro, M. X. Repka, P. Ferrone, K. Drenser, M. A. Martinez-Castellanos, S. Ostmo, K. Jonas, R. V. Chan, and M. F. Chiang, “Expert diagnosis of plus disease in retinopathy of prematurity from computer-based image analysis,” JAMA Ophthalmol. 134(6), 651–657 (2016).
[Crossref] [PubMed]

Chang, C.-C.

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Chiang, M. F.

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V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, J. Cuadros, R. Kim, R. Raman, P. C. Nelson, J. L. Mega, and D. R. Webster, “Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs,” JAMA 316(22), 2402–2410 (2016).
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Philip, A.-M.

T. Schlegl, S. M. Waldstein, H. Bogunovic, F. Endstraßer, A. Sadeghipour, A.-M. Philip, D. Podkowinski, B. S. Gerendas, G. Langs, and U. Schmidt-Erfurth, “Fully automated detection and quantification of macular fluid in OCT using deep learning,” Ophthalmology 125(4), 549–558 (2018).
[Crossref] [PubMed]

Podkowinski, D.

T. Schlegl, S. M. Waldstein, H. Bogunovic, F. Endstraßer, A. Sadeghipour, A.-M. Philip, D. Podkowinski, B. S. Gerendas, G. Langs, and U. Schmidt-Erfurth, “Fully automated detection and quantification of macular fluid in OCT using deep learning,” Ophthalmology 125(4), 549–558 (2018).
[Crossref] [PubMed]

Raman, R.

V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, J. Cuadros, R. Kim, R. Raman, P. C. Nelson, J. L. Mega, and D. R. Webster, “Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs,” JAMA 316(22), 2402–2410 (2016).
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Ren, S.

K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016), 770–778.

Repka, M. X.

J. P. Campbell, E. Ataer-Cansizoglu, V. Bolon-Canedo, A. Bozkurt, D. Erdogmus, J. Kalpathy-Cramer, S. N. Patel, J. D. Reynolds, J. Horowitz, K. Hutcheson, M. Shapiro, M. X. Repka, P. Ferrone, K. Drenser, M. A. Martinez-Castellanos, S. Ostmo, K. Jonas, R. V. Chan, and M. F. Chiang, “Expert diagnosis of plus disease in retinopathy of prematurity from computer-based image analysis,” JAMA Ophthalmol. 134(6), 651–657 (2016).
[Crossref] [PubMed]

Reynolds, J. D.

J. P. Campbell, E. Ataer-Cansizoglu, V. Bolon-Canedo, A. Bozkurt, D. Erdogmus, J. Kalpathy-Cramer, S. N. Patel, J. D. Reynolds, J. Horowitz, K. Hutcheson, M. Shapiro, M. X. Repka, P. Ferrone, K. Drenser, M. A. Martinez-Castellanos, S. Ostmo, K. Jonas, R. V. Chan, and M. F. Chiang, “Expert diagnosis of plus disease in retinopathy of prematurity from computer-based image analysis,” JAMA Ophthalmol. 134(6), 651–657 (2016).
[Crossref] [PubMed]

Rokem, A.

S. Xiao, F. Bucher, Y. Wu, A. Rokem, C. S. Lee, K. V. Marra, R. Fallon, S. Diaz-Aguilar, E. Aguilar, M. Friedlander, and A. Y. Lee, “Fully automated, deep learning segmentation of oxygen-induced retinopathy images,” JCI Insight 2(24), e97585 (2017).
[Crossref] [PubMed]

Rosner, B.

A. C. Lee, P. Panchal, L. Folger, H. Whelan, R. Whelan, B. Rosner, H. Blencowe, and J. E. Lawn, “Diagnostic Accuracy of Neonatal Assessment for Gestational Age Determination: A Systematic Review,” Pediatrics 140(6), e20171423 (2017).
[Crossref] [PubMed]

Roy, A. G.

Rudolph, A. J.

H. M. Hittner, N. J. Hirsch, and A. J. Rudolph, “Assessment of gestational age by examination of the anterior vascular capsule of the lens,” J. Pediatr. 91(3), 455–458 (1977).
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T. Schlegl, S. M. Waldstein, H. Bogunovic, F. Endstraßer, A. Sadeghipour, A.-M. Philip, D. Podkowinski, B. S. Gerendas, G. Langs, and U. Schmidt-Erfurth, “Fully automated detection and quantification of macular fluid in OCT using deep learning,” Ophthalmology 125(4), 549–558 (2018).
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V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, J. Cuadros, R. Kim, R. Raman, P. C. Nelson, J. L. Mega, and D. R. Webster, “Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs,” JAMA 316(22), 2402–2410 (2016).
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V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, J. Cuadros, R. Kim, R. Raman, P. C. Nelson, J. L. Mega, and D. R. Webster, “Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs,” JAMA 316(22), 2402–2410 (2016).
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V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, J. Cuadros, R. Kim, R. Raman, P. C. Nelson, J. L. Mega, and D. R. Webster, “Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs,” JAMA 316(22), 2402–2410 (2016).
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Figures (11)

Fig. 1
Fig. 1 Overview of algorithm for classification of pediatric eye videos by ALCV.
Fig. 2
Fig. 2 Sample imaging apparatus and video frames of 32-week, 6-day gestational aged neonate. (a) The imaging device consisted of a PanOptic ophthalmoscope using a Welch Allyn iExaminer attached to an iPhone 6 Plus. Sample raw video frames show (b) a frame, with a clear eye region, that was chosen by the automatic method, (c) frame with no eye, (d) frame with closed eye, and (e) frame with eye out of focus.
Fig. 3
Fig. 3 Preprocessing of sample input frames (a) with visible lens and (b) without visible lens. (c) Normalized image of frame (a) in which the lens-iris boundary (red arrow) and iris-skin boundary (blue arrow) are visible. (d) Normalized image of frame (b) where anatomical eye boundaries are not visible. (e, f) Edge profiles of frames (a) and (b), respectively. Convex, circular regions corresponding to lens in (e) are not visible in (f).
Fig. 4
Fig. 4 Example of CALL overestimating lens region (a) Sample frame extracted from video with (b) CALL bounding box (blue) and FALL bounding box (green). (c, d) Anterior capsule lens region segmented by CALL and FALL, respectively.
Fig. 5
Fig. 5 Example of CALL missing lens region (a) Sample frame extracted from video with (b) CALL bounding box (blue) and FALL bounding box (green) (c, d) Anterior capsule lens region segmented by CALL and FALL, respectively.
Fig. 6
Fig. 6 Sample segmented anterior lens capsule image data. Gestational age is indicated under each sample image in weeks (“w”) and days (“d”).
Fig. 7
Fig. 7 Feature Extraction and Classification Pipeline – (a) resized images of anterior lens region, (b) feature extraction (Resnet-152) [38], (c) kernel-based SVM training, (d) leave-one-out validation.
Fig. 8
Fig. 8 Examples of light reflection artifacts in ALCV images.
Fig. 9
Fig. 9 Manual labelling of chosen eye frame (a) with eye region mask (b) and annotated vessels (c).
Fig. 10
Fig. 10 Scatter plot and linear fit of ALCV density vs gestational age (m = −0.4214, r = −0.7199).
Fig. 11
Fig. 11 Sample best images extracted from neonates at 26 (a), 30 (b), and 39 (c) gestational weeks. Though (a) is expected to have highest vascularity in the anterior lens capsule, by inspection, the number of vessels seems larger in (b) than seen in (a).

Tables (4)

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Table 1 Segmentation performance metrics (mean ± standard deviation, median) per frame and per subject.

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Table 2 Fraction of subjects correctly classified using manual algorithms (bold indicates highest performing metric among algorithms).

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Table 3 Fraction of subjects correctly classified during cross-validation – Automatic vs Manual (bold indicates highest performing metric among algorithms).

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Table 4 Computation details for different stages in automatic pipeline. Training times are absent for stages that did not require training.

Equations (2)

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L= 2 i=1 MN pigi i=1 MN pi+ i=1 MN gi
P= TP TP+FP , R= TP TP+FN , and DSC= 2TP 2TP+FP+FN .