Kernel regression based segmentation of optical coherence tomography images with diabetic macular edema.

Journal Article (Journal Article)

We present a fully automatic algorithm to identify fluid-filled regions and seven retinal layers on spectral domain optical coherence tomography images of eyes with diabetic macular edema (DME). To achieve this, we developed a kernel regression (KR)-based classification method to estimate fluid and retinal layer positions. We then used these classification estimates as a guide to more accurately segment the retinal layer boundaries using our previously described graph theory and dynamic programming (GTDP) framework. We validated our algorithm on 110 B-scans from ten patients with severe DME pathology, showing an overall mean Dice coefficient of 0.78 when comparing our KR + GTDP algorithm to an expert grader. This is comparable to the inter-observer Dice coefficient of 0.79. The entire data set is available online, including our automatic and manual segmentation results. To the best of our knowledge, this is the first validated, fully-automated, seven-layer and fluid segmentation method which has been applied to real-world images containing severe DME.

Full Text

Duke Authors

Cited Authors

  • Chiu, SJ; Allingham, MJ; Mettu, PS; Cousins, SW; Izatt, JA; Farsiu, S

Published Date

  • April 1, 2015

Published In

Volume / Issue

  • 6 / 4

Start / End Page

  • 1172 - 1194

PubMed ID

  • 25909003

Pubmed Central ID

  • PMC4399658

International Standard Serial Number (ISSN)

  • 2156-7085

Digital Object Identifier (DOI)

  • 10.1364/BOE.6.001172


  • eng

Conference Location

  • United States