Automated segmentation of retinal layers from optical coherence tomography images using geodesic distance

作者：

Highlights：

• We proposed an automated method that is able to segment healthy and pathological retinal layers from 2D/3D optical coherence tomography images.

• The method uses a weighted geodesic distance efficiently derived from an Eikonal equation via fast sweeping. Segmentation proceeds by solving an ordinary differential equation.

• We introduce an OCT-specific weight function into the geodesic distance framework. This is the first work on segmentation of intra-retinal layer structures using geodesic distance.

摘要

•We proposed an automated method that is able to segment healthy and pathological retinal layers from 2D/3D optical coherence tomography images.•The method uses a weighted geodesic distance efficiently derived from an Eikonal equation via fast sweeping. Segmentation proceeds by solving an ordinary differential equation.•We introduce an OCT-specific weight function into the geodesic distance framework. This is the first work on segmentation of intra-retinal layer structures using geodesic distance.

论文关键词：Optical coherence tomography,Segmentation,Geodesic distance,Eikonal equation,Partial differential equation,Ordinary differential equation,Fast sweeping

论文评审过程：Received 7 September 2016, Revised 9 April 2017, Accepted 2 July 2017, Available online 6 July 2017, Version of Record 17 July 2017.

论文官网地址：https://doi.org/10.1016/j.patcog.2017.07.004