title: Learning layer-specific edges for segmenting retinal layers with large deformations.
abstract: We present an algorithm for layer-specific edge detection in retinal optical coherence tomography images through a structured learning algorithm to reinforce traditional graph-based retinal layer segmentation. The proposed algorithm simultaneously identifies individual layers and their corresponding edges, resulting in the computation of layer-specific edges in 1 second. These edges augment classical dynamic programming based segmentation under layer deformation, shadow artifacts noise, and without heuristics or prior knowledge. We considered Duke's online data set containing 110 B-scans of 10 diabetic macular edema subjects with 8 retinal layers annotated by two experts for experimentation, and achieved a mean distance error of 1.38 pixels whereas that of the state-of-the-art was 1.68 pixels.
authors.affiliation: School of Medical Science and Technology, IIT Kharagpur, Kharagpur, India.
authors.forename: S P K
authors.initials: SP
authors.lastname: Karri
authors.affiliation: Department of Mathematics, IIT Kharagpur, Kharagpur, India.
authors.forename: Debjani
authors.initials: D
authors.lastname: Chakraborthi
authors.affiliation: School of Medical Science and Technology, IIT Kharagpur, Kharagpur, India.
authors.forename: Jyotirmoy
authors.initials: J
authors.lastname: Chatterjee
keywords: (100.6950) Tomographic image processing; (170.1610) Clinical applications; (170.4500) Optical coherence tomography; (170.6935) Tissue characterization