Annotated data analysis of three-dimensional optical coherence tomography of the retina for the creation of an intelligent database

Malakhova, K.Y., Maltsev, D.S., Kulikov, A.N., Kazak, A.A.

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Publication in Journal of Optical Technology

For Russian citation (Opticheskii Zhurnal):

Малахова Е.Ю., Мальцев Д.С., Куликов А.Н., Казак А.А. Аннотированный анализ данных трехмерной оптической когерентной томографии сетчатки для создания интеллектуальной базы данных // Оптический журнал. 2019. Т. 86. № 11. С. 59–65. http://doi.org/10.17586/1023-5086-2019-86-11-59-65

Malakhova E.Yu., Maltsev D.S., Kulikov A.N., Kazak A.A. Annotated data analysis of three-dimensional optical coherence tomography of the retina for the creation of an intelligent database [in Russian] // Opticheskii Zhurnal. 2019. V. 86. № 11. P. 59–65. http://doi.org/10.17586/1023-5086-2019-86-11-59-65

For citation (Journal of Optical Technology):

E. Yu. Malakhova, D. S. Mal’tsev, A. N. Kulikov, and A. A. Kazak, "Annotated data analysis of three-dimensional optical coherence tomography of the retina for the creation of an intelligent database," Journal of Optical Technology. 86(11), 723-728 (2019). https://doi.org/10.1364/JOT.86.000723

Abstract:

Optical coherence tomography is one of the key diagnostic methods used in ophthalmology and has a high potential for application in an automatic analysis. In this study, we collected, annotated, and analyzed 44 three-dimensional optical coherence tomography images obtained in 39 patients suffering from central serous chorioretinopathy. A semantic annotation of the pathological changes includes three classes: (1) retinal neuroepithelial detachment, (2) retinal pigment epithelium alteration, and (3) a leakage zone. Machine learning methods have been applied to distinguish classes 2 and 3 based on the brightness characteristics of optical coherent tomography images. Intra-group clustering of the class instances showed that the separation of two groups of changes in class 2 can be associated with differences in the volumetric characteristics, whereas the brightness characteristics in class 3 differ significantly depending on the age of the patients, which can be used to predict the course of the disease.

Keywords:

optical coherence tomography, retina, cluster analysis, 3D images analysis, central serous chorioretinopathy

Acknowledgements:

The authors are grateful to Yu. E. Shelepin for help in the work on this article, as well as to E. Yu. Shelepin, K. Yu. Shelepin, and R. O. Malashin (I. P. Pavlov Institute of Physiology) for making available and supporting the operation of a server for data processing and its technical support.

OCIS codes: 110.4500, 110.2960, 170.5755, 170.4470

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