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Choroid segmentation in non-EDI OCT images of multiple sclerosis patients

4 pagesPublished: February 16, 2023


Optical coherence tomography (OCT) is a non-invasive diagnostic technique that can image ocular structures. Recently, this imaging technique has been used to diagnose and monitor patients with multiple sclerosis (MS), as several clinical studies have linked the development of MS to various changes in the eye. Among the different structures, one of the relevant biomarkers for MS analysis is the choroid. Systems such as Enhanced Depth Imaging (EDI) provide detailed images of the choroid region. However, OCT images are not routinely captured using this technology unless the study is specifically focused on choroidal analysis. In this work we propose a robust approach, based on convolutional neural networks to segment the choroid in non-EDI OCT images. The results obtained show that the proposed network manages to delimit the inferior contour of the choroid in a similar way to the experts.

Keyphrases: Choroid segmentation, Convolutional Neural Network, deep learning, Multiple Sclerosis, Optical Coherence Tomography

In: Alvaro Leitao and Lucía Ramos (editors). Proceedings of V XoveTIC Conference. XoveTIC 2022, vol 14, pages 10--13

BibTeX entry
  author    = {Emilio L\textbackslash{}'opez Varela and M. Noelia Barreira Rodr\textbackslash{}'iguez and Nuria Olivier Pascual and Emma Garcia Ben and Sara Rubio Cid and Manuel F. Gonz\textbackslash{}'alez Penedo},
  title     = {Choroid segmentation in non-EDI OCT images of multiple sclerosis patients},
  booktitle = {Proceedings of V XoveTIC Conference. XoveTIC 2022},
  editor    = {Alvaro Leitao and Luc\textbackslash{}'ia Ramos},
  series    = {Kalpa Publications in Computing},
  volume    = {14},
  pages     = {10--13},
  year      = {2023},
  publisher = {EasyChair},
  bibsource = {EasyChair,},
  issn      = {2515-1762},
  url       = {},
  doi       = {10.29007/8q52}}
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