Retinal layer segmentation of macular OCT images using boundary classification

By Andrew Lang, A. Carass, M. Hauser, E. Sotirchos, Peter A. Calabresi, H. Ying, Jerry L Prince. Biomedical Optics Express, 2013

305 citations recorded by the source corpus.

Optical coherence tomography (OCT) has proven to be an essential imaging modality for ophthalmology and is proving to be very important in neurology. OCT enables high resolution imaging of the retina, both at the optic nerve head and the macula. Macular retinal layer thicknesses provide useful diagnostic information and have been shown to correlate well with measures of disease severity in several diseases. Since manual segmentation of these layers is time consuming and prone to bias, automatic segmentation methods are critical for full utilization of this technology. In this work, we build a random forest classifier to segment eight retinal layers in macular cube images acquired by OCT. The random forest classifier learns the boundary pixels between layers, producing an accurate probability map for each boundary, which is then processed to finalize the boundaries. Using this algorithm, we can accurately segment the entire retina contained in the macular cube to an accuracy of at least 4.3 microns for any of the nine boundaries. Experiments were carried out on both healthy and multiple sclerosis subjects, with no difference in the accuracy of our algorithm found between the groups.

Subfields: Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/1fd6946c249e567fe5322fb7fab1391f3df5f0f2

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Citations in the corpus

Artificial intelligence in retina. (2018, background): “Lang et al. applied RF to segment eight layers in OCT volumes, mainly based on the position of each pixel in the volume and its gray value (Lang et al., 2013).”
Automatic segmentation of nine retinal layer boundaries in OCT images of non-exudative AMD patients using deep learning and graph search. (2017, methodology): “Following the intensity normalization method in [32], we first rescale the intensity values of the B-scan X, X I , to be between [0, 1] as follows: where max I and min I stand for the maximum and minimum values in the B-scan X, respectively.”
Joint Optic Disc and Cup Segmentation Based on Multi-Label Deep Network and Polar Transformation (2018, methodology): “This layer-like spatial structure is convenient to use, especially some layer-based segmentation methods [36], [37] can be employed as the post-processing.”