Supervised learning and dimension reduction techniques for quantification of retinal fluid in optical coherence tomography images

Eye (Lond). 2017 Aug;31(8):1212-1220. doi: 10.1038/eye.2017.61. Epub 2017 Apr 21.

Abstract

PurposeThe purpose of the present study is to develop fast automated quantification of retinal fluid in optical coherence tomography (OCT) image sets.MethodsWe developed an image analysis pipeline tailored towards OCT images that consists of five steps for binary retinal fluid segmentation. The method is based on feature extraction, pre-segmention, dimension reduction procedures, and supervised learning tools.ResultsFluid identification using our pipeline was tested on two separate patient groups: one associated to neovascular age-related macular degeneration, the other showing diabetic macular edema. For training and evaluation purposes, retinal fluid was annotated manually in each cross-section by human expert graders of the Vienna Reading Center. Compared with the manual annotations, our pipeline yields good quantification, visually and in numbers.ConclusionsBy demonstrating good automated retinal fluid quantification, our pipeline appears useful to expert graders within their current grading processes. Owing to dimension reduction, the actual learning part is fast and requires only few training samples. Hence, it is well-suited for integration into actual manufacturer's devices, further improving segmentation by its use in daily clinical life.

MeSH terms

  • Diabetic Retinopathy / diagnosis*
  • Exudates and Transudates*
  • Humans
  • Image Interpretation, Computer-Assisted / methods*
  • Macular Degeneration / diagnosis*
  • Macular Edema / diagnosis*
  • Retina
  • Retinal Neovascularization / diagnosis*
  • Supervised Machine Learning*
  • Tomography, Optical Coherence / methods*