research-article

Multi-view Learning over Retinal Thickness and Visual Sensitivity on Glaucomatous Eyes

Authors:

Toshimitsu Uesaka,

Hiroki Sugiura,

Hiroshi Murata,

Kenji YamanishiAuthors Info & Claims

KDD '17: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Pages 2041 - 2050

https://doi.org/10.1145/3097983.3098194

Published: 13 August 2017 Publication History

Abstract

Dense measurements of visual-field, which is necessary to detect glaucoma, is known as very costly and labor intensive. Recently, measurement of retinal-thickness can be less costly than measurement of visual-field. Thus, it is sincerely desired that the retinal-thickness could be transformed into visual-sensitivity data somehow. In this paper, we propose two novel methods to estimate the sensitivity of the visual-field with SITA-Standard mode 10-2 resolution using retinal-thickness data measured with optical coherence tomography (OCT). The first method called Affine-Structured Non-negative Matrix Factorization (ASNMF) which is able to cope with both the estimation of visual-field and the discovery of deep glaucoma knowledge. While, the second is based on Convolutional Neural Networks (CNNs) which demonstrates very high estimation performance. These methods are kinds of multi-view learning methods because they utilize visual-field and retinal thickness data simultaneously. We experimentally tested the performance of our methods from several perspectives. We found that ASNMF worked better for relatively small data size while CNNs did for relatively large data size. In addition, some clinical knowledge are discovered via ASNMF. To the best of our knowledge, this is the first paper to address the dense estimation of the visual-field based on the retinal-thickness data.

References

[1]

C. Ajtony, Z. Balla, S. Somoskeoy, and B. Kovacs. "Relationship between Visual Field Sensitivity and Retinal Nerve Fiber Layer Thickness as Measured by Optical Coherence Tomography", Invest. Ophth. Vis. Sci. 48(1), p. 258, 2007.

[2]

R. Asaoka. "The relationship between visual acuity and central visual field sensitivity in advanced glaucoma." Brit. J. Ophthalmol. 97(10), pp. 1355--1356, 2013.

[3]

A. Cichocki, R. Zdunek, A. H. Phan, and S. Amari. Nonnegative Matrix and Tensor Factorizations, John Wiley & Son, 2009.

Digital Library

[4]

C. Cortes and V. Vapnik. "Support vector networks", Machine Learning 20(3):273--297, 1995.

Digital Library

[5]

M. Eura, "Correspondence Between Visual Field Test Results and GCL+IPL Thickness in the Maculae of Glaucomatous Eyes", Ph.D. Thesis, Kinki University, 2014 (written in Japanese).

[6]

R. Girshick, J. Donahue, T. Darrell, and J. Malik. "Rich feature hierarchies for accurate object detection and semantic segmentation", In CVPR, pp. 580--587, 2014.

Digital Library

[7]

X. Glorot and Y. Bengio. "Understanding the difficulty of training deep feedforward neural networks", In AISTATS Vol. textbf9, pp. 249--256, 2010.

[8]

M. Higaki, K. Morino, H. Murata, R. Asaoka, and K. Yamanishi, "Predicting Glaucoma Visual Field Loss by Hierarchically Aggregating Clustering-based Predictors," arXiv:1603.07094, 2016.

[9]

C. Holmin and C. E. T. Krakau, "Regression analysis of the central visual field in chronic glaucoma cases", Acta Ophthalmologica 60(2), pp. 267 -- 274, 1982.

[10]

D. C. Hood and R. H. Kardon. "A framework for comparing structural and functional measures of glaucomatous damage", Progress in Retinal and Eye Research 26(6), pp. 688--710, 2007.

[11]

D. Huang, E. A. Swanson, C. P. Lin, J. S. Schuman, W. G. Stinson, W. Chang, M. R. Hee, T. Flotte, K. Gregory, C. A. Puliafito, and J. G. Fujimoto. "Optical coherence tomography", Science 254(5035), pp. 1178--1181, 1991.

[12]

S. Izumi, S. Shiroaki, S. Matsumoto, and M. Araie. "The relationship between visual disability and visual field in patients with glaucoma". Ophthalmology textbf110(2), pp. 332--339, 2003.

[13]

A. Kanamori, M. Nakamura, M. F.T Escano, R. Seya, H. Maeda, and A. Negi. "Evaluation of the Glaucomatous Damage on Retinal Nerve Fiber Layer Thickness Measured by Optical Coherence Tomography". Am. J. Ophthalmol. 135(4), pp. 513--520, 2003.

[14]

S. Kingman. "Glaucoma is second leading cause of blindness globally", B. World health Organ. 82(11), pp. 887--888, 2004.

[15]

Y. Kita, R. Kita, A. Takeyama, A. Anraku, G. Tomita, and I. Goldberg. "Relationship between macular ganglion cell complex thickness and macular outer retinal thickness: a spectral-domain optical coherence tomography study", Clinical & Experimental Ophthalmology 41(7), pp. 674--682, 2013.

[16]

A. Krizhevsky, I. Sutskever, and G. E. Hinton. "Imagenet classification with deep convolutional neural networks", In NIPS, pp. 1097--1105, 2012.

Digital Library

[17]

H. Laurberg and L. K. Hansen. "On Affine Non-Negative Matrix Factorization", In ICASSP 2007, Vol. II, pp. 653 - 656, 2007.

[18]

H. Laurberg, M. N. Schmidt, M. G. Christensen, and S. H. Jensen, "Structured non-negative matrix factorization with sparsity patterns", in ASILOMAR 2008, pp.1693--1697, 2008.

[19]

D. D. Lee and H. S. Seung. "Algorithms for Non-negative Matrix Factorization", in NIPS, pp. 556--562, 2000.

Digital Library

[20]

Z. Liang, R. Tomioka, H. Murata, R. Asaoka, and K. Yamanishi. "Quantitative Prediction of Glaucomatous Visual Field Loss from Few Measurements", in ICDM2013, pp. 1121--1126, 2013.

[21]

M. Lin, Q. Chen, and S. Yan. "Network in Network.", arXiv:1312.4400, 2013.

[22]

J. Liu, C. Wang, J. Gao, and J. Han. "Multi-View Clustering via Joint Non-negative Matrix Factorization", in SDM2013, pp. 252--260, 2013.

[23]

S. Maya, K. Morino, H. Murata, R. Asaoka, and K. Yamanishi. "Discovery of Glaucoma Progressive Patterns Using Hierarchical MDL-Based Clustering", in KDD 2015, pp. 1979--1988, 2015.

Digital Library

[24]

S. Maya, K. Morino, and K. Yamanishi, "Predicting Glaucoma Progression using Multi-task Learning with Heterogeneous Features", in IEEE BigData 2014, pp. 261--270, 2014.

[25]

J. Nayak, R. Acharya U., P. S. Bhat, N. Shetty, and T. C. Lim. "Automated Diagnosis of Glaucoma Using Digital Fundus Images", Journal of Medical Systems 33(5), pp. 337--346, 2009.

Digital Library

[26]

B. N. Noureddin, D. Poinoosawmy, F. W. Fietzke, and R. A. Hitchings. "Regression Analysis of Visual Field Progression in Low Tension Glaucoma", Brit. J. Ophthalmol. 75(8), pp. 493--495, 1991.

[27]

O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei. "ImageNet Large Scale Visual Recognition Challenge", Int. J. Comput. Vision 115(3), pp. 211--252, 2015.

Digital Library

[28]

T. M. Shaarawy, M. B. Sherwood, R. A. Hitchings, and J. G. Crowston. GLAUCOMA MEDICAL DIAGNOSIS & THERAPY VOLUME ONE, Elsevier, 2009.

[29]

K. Simonyan and A. Zisserman. "Very Deep Convolutional Networks for Large-Scale Image Recognition", arXiv:1409.1556, 2015.

[30]

W. H. Swanson, J. Felius, and F. Pan. "Perimetric Defects and Ganglion Cell Damage: Interpreting Linear Relations Using a Two-Stage Neural Model", Invest. Ophth. Vis. Sci. 45(2), p. 466, 2004.

[31]

C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, "Going Deeper with Convolutions", in CVPR, pp. 1--9, 2015.

[32]

K. Tomoda, K. Morino, H. Murata, R. Asaoka, and K. Yamanishi. "Predicting Glaucomatous Progression with Piecewise Regression Model from Heterogeneous Medical Data", in HEALTHINF 2016, pp. 93--104, 2016.

Digital Library

[33]

Y.-X. Wang and Y.-J. Zhang, "Nonnegative Matrix Factorization: A Comprehensive Review", IEEE Trans. Knowl. Data Eng. 25(6), pp. 1336--1353, 2013.

Digital Library

[34]

C. Xu, D. Tao, and C. Xu. "A Survey on Multi-view Learning", arXiv:1304.5634, 2013.

[35]

S. Yousefi, M. H. Goldbaum, M. Balasubramanian, T. P. Jung, R. N. Weinreb, F. A. Medeiros, L. M. Zangwill, J. M. Liebmann, C. A. Girkin, and C. Bowd. "Glaucoma Progression Detection Using Structural Retinal Nerve Fiber Layer Measurements and Functional Visual Field Points", IEEE T. Bio-med. Eng. 61(4), pp. 1143--1154, 2014.

[36]

S. Yousefi, M. H. Goldbaum, M. Balasubramanian, F. A. Medeiros, L. M. Zangwill, J. M. Liebmann, C. A. Girkin, R. N. Weinreb, and C. Bowd, "Learning From Data: Recognizing Glaucomatous Defect Patterns and Detecting Progression From Visual Field Measurements", IEEE T. Bio-med Eng. 61(7), pp. 2112- 2124, 2014.

Cited By

Pham QHan JPark DShin J(2023)Multimodal Deep Learning Model of Predicting Future Visual Field for Glaucoma PatientsIEEE Access10.1109/ACCESS.2023.324806511(19049-19058)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3248065
Xu LAsaoka RKiwaki TMurata HFujino YYamanishi KZhu FChin Ooi BMiao CWang HSkrypnyk IHsu WChawla S(2021)PAMI: A Computational Module for Joint Estimation and Progression Prediction of GlaucomaProceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining10.1145/3447548.3467195(3826-3834)Online publication date: 14-Aug-2021
https://dl.acm.org/doi/10.1145/3447548.3467195
Chen ZWollstein GSchuman JIshikawa H(2021)AI and GlaucomaArtificial Intelligence in Ophthalmology10.1007/978-3-030-78601-4_9(113-125)Online publication date: 14-Oct-2021
https://doi.org/10.1007/978-3-030-78601-4_9
Show More Cited By

Index Terms

Multi-view Learning over Retinal Thickness and Visual Sensitivity on Glaucomatous Eyes
1. Applied computing
  1. Life and medical sciences
    1. Health informatics
2. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Factorization methods
        Non-negative matrix factorization
      2. Neural networks

Recommendations

Estimating Glaucomatous Visual Sensitivity from Retinal Thickness with Pattern-Based Regularization and Visualization
KDD '18: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

Conventionally, glaucoma is diagnosed on the basis of visual field sensitivity (VF). However, the VF test is time-consuming, costly, and noisy. Using retinal thickness (RT) for glaucoma diagnosis is currently desirable. Thus, we propose a new ...
Deep multiple instance learning for automatic glaucoma prevention and auto-annotation using color fundus photography
Abstract
In the area of ophthalmology, glaucoma affects an increasing number of people. It is a major cause of blindness. Early detection prevents severe ocular complications such as glaucoma, cystoid macular edema, or diabetic proliferative retinopathy. ...
A framework for detecting glaucomatous progression in the optic nerve head of an eye using proper orthogonal decomposition
Special section on computational intelligence in medical systems

Glaucoma is the second leading cause of blindness worldwide. Often, the optic nerve head (ONH) glaucomatous damage and ONH changes occur prior to visual field loss and are observable in vivo. Thus, digital image analysis is a promising choice for ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

KDD '17: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 2017

2240 pages

ISBN:9781450348874

DOI:10.1145/3097983

General Chairs:
Stan Matwin
Dalhousie University
,
Shipeng Yu
LinkedIn
,
Faisal Farooq
IBM

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 August 2017

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

JST CREST
JSPS KAKENHI

Conference

KDD '17

Sponsor:

KDD '17: The 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 13 - 17, 2017

NS, Halifax, Canada

Acceptance Rates

KDD '17 Paper Acceptance Rate 64 of 748 submissions, 9%;

Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

5
Total Citations
View Citations
304
Total Downloads

Downloads (Last 12 months)9
Downloads (Last 6 weeks)1

Reflects downloads up to 10 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Pham QHan JPark DShin J(2023)Multimodal Deep Learning Model of Predicting Future Visual Field for Glaucoma PatientsIEEE Access10.1109/ACCESS.2023.324806511(19049-19058)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3248065
Xu LAsaoka RKiwaki TMurata HFujino YYamanishi KZhu FChin Ooi BMiao CWang HSkrypnyk IHsu WChawla S(2021)PAMI: A Computational Module for Joint Estimation and Progression Prediction of GlaucomaProceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining10.1145/3447548.3467195(3826-3834)Online publication date: 14-Aug-2021
https://dl.acm.org/doi/10.1145/3447548.3467195
Chen ZWollstein GSchuman JIshikawa H(2021)AI and GlaucomaArtificial Intelligence in Ophthalmology10.1007/978-3-030-78601-4_9(113-125)Online publication date: 14-Oct-2021
https://doi.org/10.1007/978-3-030-78601-4_9
Zheng YXu LKiwaki TWang JMurata HAsaoka RYamanishi KTeredesai AKumar VLi YRosales RTerzi EKarypis G(2019)Glaucoma Progression Prediction Using Retinal Thickness via Latent Space Linear RegressionProceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3292500.3330757(2278-2286)Online publication date: 25-Jul-2019
https://dl.acm.org/doi/10.1145/3292500.3330757
Sugiura HKiwaki TYousefi SMurata HAsaoka RYamanishi KGuo YFarooq F(2018)Estimating Glaucomatous Visual Sensitivity from Retinal Thickness with Pattern-Based Regularization and VisualizationProceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining10.1145/3219819.3219866(783-792)Online publication date: 19-Jul-2018
https://dl.acm.org/doi/10.1145/3219819.3219866

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents