ArticleTranslational vision science & technology2017
A Digital Staining Algorithm for Optical Coherence Tomography Images of the Optic Nerve Head.
Article in Translational vision science & technology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 9 citations in OpenAlex.
- Compression OCT-elastography combined with speckle-contrast analysis as an approach to the morphological assessment of breast cancer tissue.Biomedical optics express · 2023Article
- Machine-learning based segmentation of the optic nerve head using multi-contrast Jones matrix optical coherence tomography with semi-automatic training dataset generation.Biomedical optics express · 2018Article
- DRUNET: a dilated-residual U-Net deep learning network to segment optic nerve head tissues in optical coherence tomography images.Biomedical optics express · 2018Article
- A new strategy to interpret OCT posterior pole asymmetry analysis for glaucoma diagnosis.International journal of ophthalmology · 2017Article
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Authors and funding
5 authors at 4 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo digitally stain spectral-domain optical coherence tomography (OCT) images of the optic nerve head (ONH), and highlight either connective or neural tissues.
methodsOCT volumes of the ONH were acquired from one eye of 10 healthy subjects. We processed all volumes with adaptive compensation to remove shadows and enhance deep tissue visibility. For each ONH, we identified the four most dissimilar pixel-intensity histograms, each of which was assumed to represent a tissue group. These four histograms formed a vector basis on which we 'projected' each OCT volume in order to generate four digitally stained volumes P1 to P4. Digital staining was also verified using a digital phantom, and compared with k-means clustering for three and four clusters.
resultsDigital staining was able to isolate three regions of interest from the proposed phantom. For the ONH, the digitally stained images P1 highlighted mostly connective tissues, as demonstrated through an excellent contrast increase across the anterior lamina cribrosa boundary (3.6 ± 0.6 times). P2 highlighted the nerve fiber layer and the prelamina, P3 the remaining layers of the retina, and P4 the image background. Further, digital staining was able to separate ONH tissue layers that were not well separated by k-means clustering.
conclusionWe have described an algorithm that can digitally stain connective and neural tissues in OCT images of the ONH. TRANSLATIONAL RELEVANCE: Because connective and neural tissues are considerably altered in glaucoma, digital staining of the ONH tissues may be of interest in the clinical management of this pathology.
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