Automated Quantification of Hyperreflective Foci in SD-OCT With Diabetic Retinopathy
| Autor: | Loza Bekalo, Wen Fan, Qiang Chen, Idowu Paul Okuwobi, Zexuan Ji, Songtao Yuan |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
medicine.medical_specialty
genetic structures Correlation coefficient Databases Factual 02 engineering and technology Macular Edema 03 medical and health sciences chemistry.chemical_compound 0302 clinical medicine Health Information Management Optical coherence tomography Region of interest Ophthalmology Histogram Image Interpretation Computer-Assisted 0202 electrical engineering electronic engineering information engineering medicine Humans Electrical and Electronic Engineering Diabetic Retinopathy integumentary system medicine.diagnostic_test business.industry Retinal Diabetic retinopathy Image segmentation medicine.disease eye diseases Computer Science Applications body regions chemistry 030221 ophthalmology & optometry 020201 artificial intelligence & image processing business Algorithms Tomography Optical Coherence Biotechnology Retinopathy |
| Zdroj: | IEEE journal of biomedical and health informatics. 24(4) |
| ISSN: | 2168-2208 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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