Automated Quantification of Hyperreflective Foci in SD-OCT With Diabetic Retinopathy
<|reference_start|>Automated Quantification of Hyperreflective Foci in SD-OCT With Diabetic Retinopathy: The presence of hyperreflective foci (HFs) is related to retinal disease progression, and the quantity has proven to be a prognostic factor of visual and anatomical outcome in various retinal diseases. However, lack of efficient quantitative tools for evaluating the HFs has deprived ophthalmologist of assessing the volume of HFs. For this reason, we propose an automated quantification algorithm to segment and quantify HFs in spectral domain optical coherence tomography (SD-OCT). The proposed algorithm consists of two parallel processes namely: region of interest (ROI) generation and HFs estimation. To generate the ROI, we use morphological reconstruction to obtain the reconstructed image and histogram constructed for data distributions and clustering. In parallel, we estimate the HFs by extracting the extremal regions from the connected regions obtained from a component tree. Finally, both the ROI and the HFs estimation process are merged to obtain the segmented HFs. The proposed algorithm was tested on 40 3D SD-OCT volumes from 40 patients diagnosed with non-proliferative diabetic retinopathy (NPDR), proliferative diabetic retinopathy (PDR), and diabetic macular edema (DME). The average dice similarity coefficient (DSC) and correlation coefficient (r) are 69.70%, 0.99 for NPDR, 70.31%, 0.99 for PDR, and 71.30%, 0.99 for DME, respectively. The proposed algorithm can provide ophthalmologist with good HFs quantitative information, such as volume, size, and location of the HFs.<|reference_end|>
@article{okuwobi2024automated,
    title={Automated Quantification of Hyperreflective Foci in SD-OCT With Diabetic
  Retinopathy},
    author={Idowu Paul Okuwobi, Zexuan Ji, Wen Fan, Songtao Yuan, Loza Bekalo, and
  Qiang Chen},
    journal={IEEE Journal of Biomedical and Health Informatics, Volume: 24,
  Issue: 4, pp. 1125 - 1136, 2020},
    year={2024},
    doi={10.1109/JBHI.2019.2929842},
    archivePrefix={arXiv},
    eprint={2407.21272},
    primaryClass={cs.AI cs.CV}
}