Improved Accuracy and Robustness of a Corneal Endothelial Cell Segmentation Method Based on Merging Superpixels

Juan P. Vigueras-Guillén, Angela Engel, Hans G. Lemij, Jeroen van Rooij, Koenraad A. Vermeer, Lucas J. van Vliet

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review

Abstract

Clinical parameters related to the corneal endothelium can only be estimated by segmenting endothelial cell images. Specular microscopy is the current standard technique to image the endothelium, but its low SNR make the segmentation a complicated task. Recently, we proposed a method to segment such images by starting with an oversegmented image and merging the superpixels that constitute a cell. Here, we show how our merging method provides better results than optimizing the segmentation itself. Furthermore, our method can provide accurate results despite the degree of the initial oversegmentation, resulting into a precision and recall of 0.91 for the optimal oversegmentation.
Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
PublisherSpringer Verlag
Pages631-638
Number of pages8
ISBN (Print)9783319929996
DOIs
Publication statusPublished - 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10882 LNCS

Keywords

  • Oversegmentation
  • Specular microscopy
  • Watershed

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