IEEE Access (Jan 2021)

ChoroidNET: A Dense Dilated U-Net Model for Choroid Layer and Vessel Segmentation in Optical Coherence Tomography Images

  • Tin Tin Khaing,
  • Takayuki Okamoto,
  • Chen Ye,
  • Md. Abdul Mannan,
  • Hirotaka Yokouchi,
  • Kazuya Nakano,
  • Pakinee Aimmanee,
  • Stanislav S. Makhanov,
  • Hideaki Haneishi

DOI
https://doi.org/10.1109/ACCESS.2021.3124993
Journal volume & issue
Vol. 9
pp. 150951 – 150965

Abstract

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Understanding the changes in choroidal thickness and vasculature is important to monitor the development and progression of various ophthalmic diseases. Accurate segmentation of the choroid layer and choroidal vessels is critical to better analyze and understand the choroidal changes. In this study, we develop a dense dilated U-Net model (ChoroidNET) for segmenting the choroid layer and choroidal vessels in optical coherence tomography (OCT) images. The performance of ChoroidNET is evaluated using an OCT dataset that contains images with various retinal pathologies. Overall Dice coefficient of 95.1 ± 0.4 and 82.4 ± 2.4 were obtained for choroid layer and vessel segmentation, respectively. Comparisons show that among state-of-the-art models, ChoroidNET, which produces results that are consistent with ground truths, is the most robust segmentation framework.

Keywords