Automated assessment of the smoothness of retinal layers in optical coherence tomography images using a machine learning algorithm

BMC Medical Imaging

Research

Alireza Afzal Aghaei¹  Tahereh Mahmoudi²  Zahra Montazeriani²  Jamshid Saeidian³  Hossein Azimi³  Mohammad Zarei⁴  Behzad Jafari⁴  Nazanin Ebrahimiadib⁴  Hamid Riazi-Esfahani⁴  Elias Khalili Pour⁴  Alireza Khodabande⁴ 

[1] Department of Computer Sciences, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran, Iran;Department of Medical Physics and Biomedical Engineering, Tehran University of Medical Sciences and Research Center for Science and Technology in Medicine, Tehran, Iran;Faculty of Mathematical Sciences and Computer, Kharazmi University, No. 50, Taleghani Ave, Tehran, Iran;Retina Service, Farabi Eye Hospital, Tehran University of Medical Sciences, Tehran, Iran;

关键词: Automated segmentation;    Support vector regression;    Inner plexiform layer;    Outer plexiform Layer;    Bland-Altman plot;    Biomarker;   

DOI  :  10.1186/s12880-023-00976-w

 received in 2022-06-26, accepted in 2023-01-25,  发布年份 2023

来源: Springer

【 摘 要 】

Quantifying the smoothness of different layers of the retina can potentially be an important and practical biomarker in various pathologic conditions like diabetic retinopathy. The purpose of this study is to develop an automated machine learning algorithm which uses support vector regression method with wavelet kernel and automatically segments two hyperreflective retinal layers (inner plexiform layer (IPL) and outer plexiform layer (OPL)) in 50 optical coherence tomography (OCT) slabs and calculates the smoothness index (SI). The Bland–Altman plots, mean absolute error, root mean square error and signed error calculations revealed a modest discrepancy between the manual approach, used as the ground truth, and the corresponding automated segmentation of IPL/ OPL, as well as SI measurements in OCT slabs. It was concluded that the constructed algorithm may be employed as a reliable, rapid and convenient approach for segmenting IPL/OPL and calculating SI in the appropriate layers.

【 授权许可】

CC BY   
© The Author(s) 2023

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