Abstract
Many studies in the rigid gas permeable (RGP) lens fitting field have focused on providing the best fit for patients with irregular astigmatism, a challenging issue. Despite the ease and accuracy of fitting in the current fitting methods, no studies have provided a high-pace solution with the final best fit to assist experts. This work presents a deep learning solution for identifying features in Pentacam four refractive maps and RGP base curve identification. An authentic dataset of 247 samples of Pentacam four refractive maps was gathered, providing a multi-view image of the corneal structure. Scratch-based convolutional neural network (CNN) architectures and well-known CNN architectures such as AlexNet, GoogLeNet, and ResNet have been used to extract features and transfer learning. Features are aggregated through a fusion technique. Based on a comparison of means square error (MSE) of normalized labels, the multi-view scratch-based CNN provided R-squared of 0.849, 0.846, 0.835, and 0.834 followed by GoogLeNet, comparable with current methods. Transfer learning outperforms various scratch-based CNN models, through which proper specifications some scratch-based models were able to increase coefficient of determinations. CNNs on multi-view Pentacam images have enabled fast detection of the RGP lens base curve, higher patient satisfaction, and reduced chair time.
Similar content being viewed by others
References
Anderson D, Chang C, Kusy P, Olafsson HE, Roddy D, Ziémba SL (2013) Clinical white paper- case reports: the UltraHealth silicone hydrogel contact lens for keratoconus and irregular corneas, Carlsbad, CA
Mathews SM, Bradley JC, George JG, Xu KT (2005) Predicting contact lens base curve using corneal topography in keratoconus. Invest Ophthalmol Vis Sci 46:18
Yildiz EH, Erdurmus M, Elibol ES, Acar B, Vural ET (2015) Contact lens impact on quality of life in keratoconus patients: rigid gas permeable versus soft silicone-hydrogel keratoconus lenses. Int J Ophthalmol 8(5):1074–1077
Alkhaldi W (2010) Statistical signal and image processing techniques in corneal modeling. TU Darmstadt
Alió JL, Belda JI, Artola A, García-Lledó M, Osman A (2002) Contact lens fitting to correct irregular astigmatism after corneal refractive surgery. J Cataract Refract Surg 28(10):1750–1757
Jain R, Grewal S (2009) Pentacam: principle and clinical applications. J Curr Glaucoma Pract 3(2):20–32
Miranda MA, Radhakrishnan H, O’Donnell C (2009) Repeatability of oculus pentacam metrics derived from corneal topography. Cornea 28(6):657–666
Hashemi H, Mehravaran S (2010) Day to day clinically relevant corneal elevation, thickness, and curvature parameters using the orbscan II scanning slit topographer and the pentacam scheimpflug imaging device. Middle East Afr J Ophthalmol 17(1):44
Nosch DS, Ong GL, Mavrikakis I, Morris J (2007) The application of a computerised videokeratography (CVK) based contact lens fitting software programme on irregularly shaped corneal surfaces. Cont Lens Anterior Eye 30(4):239–248
Siddireddy JS, Mahadevan R (2013) Comparison of conventional method of contact lens fitting and software based contact lens fitting with Medmont corneal topographer in eyes with corneal scar. Contact Lens Anterior Eye 36(4):176–181
Ortiz-Toquero S, Rodriguez G, de Juan V, Martin R (2016) Rigid gas permeable contact lens fitting using new software in keratoconic eyes. Optom Vis Sci 93(3):286–292
Ortiz-Toquero S, Rodriguez G, de Juan V, Martin R (2017) New web-based algorithm to improve rigid gas permeable contact lens fitting in keratoconus. Contact Lens Anterior Eye 40(3):143–150
Zhao F, Wang J, Wang L, Chen L (2018) An approach for simulating the fitting of rigid gas-permeable contact lenses using 3D printing technology. Contact Lens Anterior Eye
Wang K, Zhou S, Fu CA, Yu JX (2003) Mining changes of classification by correspondence tracing, in Proceedings of the 2003 SIAM International Conference on Data Mining, pp 95–106
Awadalla M, El-Far S (2012) Aggregate function based enhanced a priori algorithm for mining association rules. Int J Comput Sci Issues 9(3):277–287
Mohammed E (2013) A framework intelligent mobile for diagnosis contact lenses by applying case based reasoning. In: Innovations and advances in computer, information, systems sciences, and engineering. Springer, pp 1233–1238
Dua D, Karra Taniskidou E UCI machine learning repository. University of California, School of Information and Computer Science, Irvine
Cardona G, Isern R (2011) Topography-based RGP lens fitting in normal corneas: the relevance of eyelid and tear film attributes. Eye Contact Lens Sci Clin Pract 37(6):359–364
Rajabi MT, Mohajernezhad-Fard Z, Naseri SK, Jafari F, Doostdar A, Zarrinbakhsh P, Rajabi MB, Kohansal S (2011) Rigid contact lens fitting based on keratometry readings in keratoconus patients: predicting formula. Int J Ophthalmol 4(5):525–528
Pang S, Du A, Orgun MA, Yu Z (2019) A novel fused convolutional neural network for biomedical image classification. Med Biol Eng Comput 57(1):107–121
Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, van der Laak J, van Ginneken B, Sánchez CI (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60–88
Zu C, Zhu L, Zahng D (2017) Iterative sparsity score for feature selection and its entension for multimodal data. Neurocomputing 259:146–153
Zhao Y et al (2019) A CNN-based prototype method of unstructured surgical state perception and navigation for an endovascular surgery robot. Med Biol Eng Comput:1–13
Shin H-C, Roth HR, Gao M, Lu L, Xu Z, Nogues I, Yao J, Mollura D, Summers RM (2016) Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging 35(5):1285–1298
Zou J, Rui T, Zhou Y, Yang C, Zhang S (2018) Convolutional neural network simplification via feature map pruning. Comput Electr Eng 70:950–958
Yoo TK, Choi JY, Seo JG, Ramasubramanian B, Selvaperumal S, Kim DW (2019) The possibility of the combination of OCT and fundus images for improving the diagnostic accuracy of deep learning for age-related macular degeneration: a preliminary experiment. Med Biol Eng Comput 57(3):677–687
Tajbakhsh N, Shin JY, Gurudu SR, Hurst RT, Kendall CB, Gotway MB, Jianming Liang (2016) Convolutional neural networks for medical image analysis: full training or fine tuning? IEEE Trans Med Imaging 35(5):1299–1312
Dao TT (2019) From deep learning to transfer learning for the prediction of skeletal muscle forces. Med Biol Eng Comput 57(5):1049–1058
Mazo C, Bernal J, Trujillo M, Alegre E (2018) Transfer learning for classification of cardiovascular tissues in histological images. Comput Methods Prog Biomed 165:69–76
Pan SJ, Yang Q (Oct. 2010) A survey on transfer learning. IEEE Trans Knowl Data Eng 22(10):1345–1359
Chen C-H et al (2019) Computer-aided diagnosis of endobronchial ultrasound images using convolutional neural network. Comput Methods Prog Biomed 177:175–182
Shin HC, Lu L, Kim L, Seff A, Yao J, Summers RM (2015) Interleaved text/image deep mining on a very large-scale radiology database, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 1090–1099
Bar Y, Diamant I, Wolf L, Lieberman S, Konen E, Greenspan H (2015) Chest pathology detection using deep learning with non-medical training, in 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), pp 294–297
Huynh BQ, Li H, Giger ML (2016) Digital mammographic tumor classification using transfer learning from deep convolutional neural networks. J Med Imaging (Bellingham, Wash.) 3(3):034501
Chen H, Zheng Y, Park J-H, Heng P-A, Zhou SK (2016) Iterative multi-domain regularized deep learning for anatomical structure detection and segmentation from ultrasound images. Springer, Cham, pp 487–495
Margeta J, Criminisi A, Cabrera Lozoya R, Lee DC, Ayache N (2017) Fine-tuned convolutional neural nets for cardiac MRI acquisition plane recognition. Comput Methods Biomech Biomed Eng Imaging Vis 5(5):339–349
Karri SPK, Chakraborty D, Chatterjee J (2017) Transfer learning based classification of optical coherence tomography images with diabetic macular edema and dry age-related macular degeneration. Biomed Opt Express 8(2):579–592
Yu Y et al (2017) Deep transfer learning for modality classification of medical images. Information 8(3):91
Cheng PM, Malhi HS (2017) Transfer learning with convolutional neural networks for classification of abdominal ultrasound images. J Digit Imaging 30(2):234–243
Shahin AI, Guo Y, Amin KM, Sharawi AA (2019) White blood cells identification system based on convolutional deep neural learning networks. Comput Methods Prog Biomed 168:69–80
Jie B, Zhang D, Cheng B, Shen D (2015) Manifold regularized multitask feature learning for multimodality disease classification. Hum Brain Mapp 36(2):489–507
Hasan SA, Singh M (2014) Automatic diagnosis of astigmatism for Pentacam sagittal maps, in International Conference on Advances in Computing, Communications and Informatics (ICACCI, 2014), pp. 472–478
Faust O, Hagiwara Y, Hong TJ, Lih OS, Acharya UR (2018) Deep learning for healthcare applications based on physiological signals: a review. Comput Methods Prog Biomed 161:1–13
Krizhevsky A, Sutskever I, Hinton GE (2012) ImageNet classification with deep convolutional neural networks, in Advances in neural information processing systems 25 (NIPS 2012), pp 1097–1105
Szegedy C et al (2015) Going deeper with convolutions, in IEEE conference on computer vision and pattern recognition, pp 1–9
He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition, in Computer Vision and Pattern Recogn, pp 770–778
Alom MZ et al (2018) The history began from AlexNet: a comprehensive survey on deep learning approaches
H. M. Ahmad, S. Ghuffar, and K. Khurshid (2019) Classification of breast cancer histology images using transfer learning, in 2019 16th International Bhurban Conference on Applied Sciences and Technology (IBCAST), pp 328–332
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition
Shao L, Zhu F, Li X (2015) Transfer learning for visual categorization: a survey. IEEE Trans Neural Networks Learn Syst 26(5):1019–1034
Jorge A, Jose B, Amr O, Ahmad S (2003) Topography-guided laser in situ keratomileusis (TOPOLINK) to correct irregular astigmatism after previous refractive surgery. J Refract Surg 19(5):516–527
Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift, In 32nd International Conference on Machine Learning
Bausch and Lomb, Boston gas permeable contact lens materials, 2016. [Online]. Available: http://www.bauschsvp.com/Portals/137/assets/boston-xo-eo-es-insert.pdf. Accessed May 2019
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they do not have a conflict of interest.
Ethical approval
All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.
Informed consent
Informed consent was obtained from all individual participants included in the study.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Hashemi, S., Veisi, H., Jafarzadehpur, E. et al. Multi-view deep learning for rigid gas permeable lens base curve fitting based on Pentacam images. Med Biol Eng Comput 58, 1467–1482 (2020). https://doi.org/10.1007/s11517-020-02154-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11517-020-02154-4