A self-adaptive deep learning method for automated eye laterality detection based on color fundus photography


Autoři: Chi Liu aff001;  Xiaotong Han aff001;  Zhixi Li aff001;  Jason Ha aff003;  Guankai Peng aff004;  Wei Meng aff004;  Mingguang He aff001
Působiště autorů: State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China aff001;  School of Computer Science, University of Technology, Sydney, Australia aff002;  Faculty of Medicine, Nursing and Health Sciences, Monash University, Clayton, Australia aff003;  Guangzhou Healgoo Interactive Medical Technology Co. Ltd., Guangzhou, China aff004;  Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, Melbourne, Australia aff005;  Ophthalmology, Department of Surgery, University of Melbourne, Melbourne, Australia aff006
Vyšlo v časopise: PLoS ONE 14(9)
Kategorie: Research Article
prolekare.web.journal.doi_sk: 10.1371/journal.pone.0222025

Souhrn

Purpose

To provide a self-adaptive deep learning (DL) method to automatically detect the eye laterality based on fundus images.

Methods

A total of 18394 fundus images with real-world eye laterality labels were used for model development and internal validation. A separate dataset of 2000 fundus images with eye laterality labeled manually was used for external validation. A DL model was developed based on a fine-tuned Inception-V3 network with self-adaptive strategy. The area under receiver operator characteristic curve (AUC) with sensitivity and specificity and confusion matrix were applied to assess the model performance. The class activation map (CAM) was used for model visualization.

Results

In the external validation (N = 2000, 50% labeled as left eye), the AUC of the DL model for overall eye laterality detection was 0.995 (95% CI, 0.993–0.997) with an accuracy of 99.13%. Specifically for left eye detection, the sensitivity was 99.00% (95% CI, 98.11%-99.49%) and the specificity was 99.10% (95% CI, 98.23%-99.56%). Nineteen images were wrongly classified as compared to the human labels: 12 were due to human wrong labelling, while 7 were due to poor image quality. The CAM showed that the region of interest for eye laterality detection was mainly the optic disc and surrounding areas.

Conclusion

We proposed a self-adaptive DL method with a high performance in detecting eye laterality based on fundus images. Results of our findings were based on real world labels and thus had practical significance in clinical settings.

Klíčová slova:

Biology and life sciences – Anatomy – Head – Medicine and health sciences – Eyes – Ocular system – Ocular anatomy – Optic disc – Ophthalmology – Eye diseases – Research and analysis methods – Imaging techniques – Photography – Computer and information sciences – Software engineering – Preprocessing – Artificial intelligence – Machine learning – Deep learning – Engineering and technology – Equipment – Optical equipment – Cameras


Zdroje

1. Grassmann F, Mengelkamp J, Brandl C, Harsch S, Zimmermann ME, Linkohr B, et al. A Deep Learning Algorithm for Prediction of Age-Related Eye Disease Study Severity Scale for Age-Related Macular Degeneration from Color Fundus Photography. Ophthalmology. 2018;125(9):1410–20. doi: 10.1016/j.ophtha.2018.02.037 29653860

2. Li Z, He Y, Keel S, Meng W, Chang RT, He M. Efficacy of a Deep Learning System for Detecting Glaucomatous Optic Neuropathy Based on Color Fundus Photographs. Ophthalmology. 2018;125(8):1199–206. doi: 10.1016/j.ophtha.2018.01.023 29506863

3. Ting DSW, Cheung CY, Lim G, Tan GSW, Quang ND, Gan A, et al. Development and Validation of a Deep Learning System for Diabetic Retinopathy and Related Eye Diseases Using Retinal Images From Multiethnic Populations With Diabetes. JAMA. 2017;318(22):2211–23. doi: 10.1001/jama.2017.18152 29234807

4. Cuadros J, Sim I. EyePACS: an open source clinical communication system for eye care. Stud Health Technol Inform. 2004;107(Pt 1):207–11. 15360804

5. Ting DSW, Liu Y, Burlina P, Xu X, Bressler NM, Wong TY. AI for medical imaging goes deep. Nat Med. 2018;24(5):539–40. doi: 10.1038/s41591-018-0029-3 29736024

6. Tan NM, Liu J, Wong DWK, Lim JH, Li H, Patil SB, et al., editors. Automatic Detection of Left and Right Eye in Retinal Fundus Images. 13th International Conference on Biomedical Engineering; 2009 2009//; Berlin, Heidelberg: Springer Berlin Heidelberg.

7. Roy PK, Chakravorty R, Sedai S, Mahapatra D, Garnavi R, editors. Automatic Eye Type Detection in Retinal Fundus Image Using Fusion of Transfer Learning and Anatomical Features. 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA); 2016 30 Nov.-2 Dec. 2016.

8. Wong TY, Bressler NM. Artificial Intelligence With Deep Learning Technology Looks Into Diabetic Retinopathy Screening. JAMA. 2016;316(22):2366–7. doi: 10.1001/jama.2016.17563 27898977

9. Maimó LF, Gómez ÁLP, Clemente FJG, Pérez MG, Pérez GM. A Self-Adaptive Deep Learning-Based System for Anomaly Detection in 5G Networks. IEEE Access. 2018;6:7700–12.

10. Poplin R, Varadarajan AV, Blumer K, Liu Y, McConnell MV, Corrado GS, et al. Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nature Biomedical Engineering. 2018;2(3):158–64. doi: 10.1038/s41551-018-0195-0 31015713

11. Jin G, Ding X, Xiao W, Xu X, Wang L, Han X, et al. Prevalence of age-related macular degeneration in rural southern China: the Yangxi Eye Study. Br J Ophthalmol. 2018;102(5):625–30. doi: 10.1136/bjophthalmol-2017-310368 28848023

12. Zuiderveld K. Contrast Limited Adaptive Histogram Equalization. Graphics gems. 1994:474–85.

13. Ebner M. Color constancy based on local space average color. Machine Vision and Applications. 2009;20(5):283–301.

14. Ben G. Kaggle diabetic retinopathy detection competition report, 2015. University of Warwick. 2015.

15. Kumar T, Verma Karun. A Theory Based on Conversion of RGB image to Gray image. International Journal of Computer Applications. 2010;7(2):7–10.

16. Christian Szegedy VV, Sergey Ioffe, Jon Shlens, Zbigniew Wojna. Rethinking the Inception Architecture for Computer Vision. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2016:2818–26.

17. Bottou L. Stochastic Gradient Descent Tricks. In: Montavon G, Orr GB, Müller K-R, editors. Neural Networks: Tricks of the Trade: Second Edition. Berlin, Heidelberg: Springer Berlin Heidelberg; 2012. p. 421–36.

18. Bolei Zhou AK, Agata Lapedriza, Aude Oliva, Antonio Torralba. Learning Deep Features for Discriminative Localization. The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)2016. p. 2921–9.

19. Jang Y, Son J, Park KH, Park SJ, Jung K-H. Laterality Classification of Fundus Images Using Interpretable Deep Neural Network. Journal of Digital Imaging. 2018.

20. Liu S, Graham SL, Schulz A, Kalloniatis M, Zangerl B, Cai W, et al. A Deep Learning-Based Algorithm Identifies Glaucomatous Discs Using Monoscopic Fundus Photographs. Ophthalmology Glaucoma. 2018;1(1):15–22.

21. Hawkins DM. The Problem of Overfitting. Journal of Chemical Information and Computer Sciences. 2004;44(1):1–12. doi: 10.1021/ci0342472 14741005

22. Xiao-Hu Y, Guo-An C, Shi-Xin C. Dynamic learning rate optimization of the backpropagation algorithm. IEEE Transactions on Neural Networks. 1995;6(3):669–77. doi: 10.1109/72.377972 18263352


Článok vyšiel v časopise

PLOS One


2019 Číslo 9

Najčítanejšie v tomto čísle

Tejto téme sa ďalej venujú…


Kurzy

Zvýšte si kvalifikáciu online z pohodlia domova

nový kurz
Autori: MUDr. Petr Výborný, CSc., FEBO

Autori: MUDr. Jiří Horažďovský, Ph.D

Zánětlivá bolest zad a axiální spondylartritida – Diagnostika a referenční strategie
Autori: MUDr. Monika Gregová, Ph.D., MUDr. Kristýna Bubová

Krvácení v důsledku portální hypertenze při jaterní cirhóze – od pohledu záchranné služby až po závěrečný hepato-gastroenterologický pohled
Autori: PhDr. Petr Jaššo, MBA, MUDr. Hynek Fiala, Ph.D., prof. MUDr. Radan Brůha, CSc., MUDr. Tomáš Fejfar, Ph.D., MUDr. David Astapenko, Ph.D., prof. MUDr. Vladimír Černý, Ph.D.

Rozšíření možností lokální terapie atopické dermatitidy v ordinaci praktického lékaře či alergologa
Autori: MUDr. Nina Benáková, Ph.D.

Všetky kurzy
Prihlásenie
Zabudnuté heslo

Nemáte účet?  Registrujte sa

Zabudnuté heslo

Zadajte e-mailovú adresu, s ktorou ste vytvárali účet. Budú Vám na ňu zasielané informácie k nastaveniu nového hesla.

Prihlásenie

Nemáte účet?  Registrujte sa