Segmenting retinal OCT images with inter-B-scan and longitudinal information

Yufan He, Aaron Carass, Yihao Liu, Angeliki Filippatou, Bruno M. Jedynak, Sharon D. Solomon, Shiv Saidha, Peter A. Calabresi, Jerry L. Prince

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Monitoring retinal thickness of persons with multiple sclerosis (MS) provides important bio-markers for disease progression. However, changes in retinal thickness can be small and concealed by noise in the acquired data. Consistent longitudinal retinal layer segmentation methods for optical coherence tomography (OCT) images are crucial for identifying the real longitudinal retinal changes of individuals with MS. In this paper, we propose an iterative registration and deep learning based segmentation method for longitudinal 3D OCT scans. Since 3D OCT scans are usually anisotropic with large slice separation, we extract B-scan features using 2D deep networks and utilize inter-B-scan context with convolutional long-short-term memory (LSTM). To incorporate longitudinal information, we perform fundus registration and interpolate the smooth retinal surfaces of the previous visit to use as a prior on the current visit.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2020
Subtitle of host publicationImage Processing
EditorsIvana Isgum, Bennett A. Landman
PublisherSPIE
ISBN (Electronic)9781510633933
DOIs
StatePublished - 2020
EventMedical Imaging 2020: Image Processing - Houston, United States
Duration: Feb 17 2020Feb 20 2020

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume11313
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2020: Image Processing
CountryUnited States
CityHouston
Period2/17/202/20/20

Keywords

  • Conv-LSTM
  • Fully convolutional network
  • Longitudinal
  • OCT
  • Retina

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging

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