Chiu SJ, Li XT, Nicholas P, et al.. Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation

Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA.
Optics Express (Impact Factor: 3.49). 08/2010; 18(18):19413-28. DOI: 10.1364/OE.18.019413
Source: PubMed


Segmentation of anatomical and pathological structures in ophthalmic images is crucial for the diagnosis and study of ocular diseases. However, manual segmentation is often a time-consuming and subjective process. This paper presents an automatic approach for segmenting retinal layers in Spectral Domain Optical Coherence Tomography images using graph theory and dynamic programming. Results show that this method accurately segments eight retinal layer boundaries in normal adult eyes more closely to an expert grader as compared to a second expert grader.

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Article: Chiu SJ, Li XT, Nicholas P, et al.. Automatic segmentation of seven retinal layers in SDOCT images congruent with expert manual segmentation

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    • "Of these, the first task is relatively well posed because the RPE, defining the CIB, is significantly brighter than adjacent layers. Indeed, the gradient-based approach in various flavors has proven accurate not only in detecting CIB [22]–[28], but also in the related problem of detecting boundaries between successive retinal layers with well-defined brightness transition [33]– [36]. Accordingly, we shall also adopt a gradient-based approach for CIB detection. "
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    • "Within a given eye of an individual animal, thirty B-scans were averaged to reduce the speckle noise in the image, corresponding to lateral averaging over 10 lm. A semi-automated segmentation software developed at Duke University (Chiu et al., 2010; Lee et al., 2013) was used to calculate the thickness and average intensity of each layer. These measurements were made sequentially at the same eccentricity in the same animal over time, resulting in longitudinal data. "
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    • "We have previously demonstrated novel algorithms for pathology segmentation that may be applied to identify the rapid alterations that occur during surgical intervention [4], [39]. Automated segmentation of retinal layers has been previously described [40]. The application of similar algorithms could potentially be utilized for real-time surgical feedback on instrument-tissue proximity. "
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