6 February 2018 Automated segmentation of hyperreflective foci in spectral domain optical coherence tomography with diabetic retinopathy
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Abstract
We propose an automated segmentation method to detect, segment, and quantify hyperreflective foci (HFs) in three-dimensional (3-D) spectral domain optical coherence tomography (SD-OCT). The algorithm is divided into three stages: preprocessing, layer segmentation, and HF segmentation. In this paper, a supervised classifier (random forest) was used to produce the set of boundary probabilities in which an optimal graph search method was then applied to identify and produce the layer segmentation using the Sobel edge algorithm. An automated grow-cut algorithm was applied to segment the HFs. The proposed algorithm was tested on 20 3-D SD-OCT volumes from 20 patients diagnosed with proliferative diabetic retinopathy (PDR) and diabetic macular edema (DME). The average dice similarity coefficient and correlation coefficient ( r ) are 62.30%, 96.90% for PDR, and 63.80%, 97.50% for DME, respectively. The proposed algorithm can provide clinicians with accurate quantitative information, such as the size and volume of the HFs. This can assist in clinical diagnosis, treatment, disease monitoring, and progression.
© 2018 Society of Photo-Optical Instrumentation Engineers (SPIE)
Idowu Paul Okuwobi, Wen Fan, Chenchen Yu, Songtao Yuan, Qinghuai Liu, Yuhan Zhang, Bekalo Loza, Qiang Chen, "Automated segmentation of hyperreflective foci in spectral domain optical coherence tomography with diabetic retinopathy," Journal of Medical Imaging 5(1), 014002 (6 February 2018). https://doi.org/10.1117/1.JMI.5.1.014002 . Submission: Received: 14 June 2017; Accepted: 11 January 2018
Received: 14 June 2017; Accepted: 11 January 2018; Published: 6 February 2018
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