Macular GCIPL Thickness Map Prediction via Time-Aware Convolutional LSTM

Zhiqi Chen, Yao Wang, Gadi Wollstein, Maria De Los Angeles Ramos-Cadena, Joel Schuman, Hiroshi Ishikawa

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

Abstract

Macular ganglion cell inner plexiform layer (GCIPL) thickness is an important biomarker for clinical managements of glaucoma. Clinical analysis of GCIPL progression uses averaged thickness only, which easily washes out small changes and reveals no spatial patterns. This is the first work to predict the 2D GCIPL thickness map. We propose a novel Time-aware Convolutional Long Short-Term Memory (TC-LSTM) unit to decompose memories into the short-term and long-term memories and exploit time intervals to penalize the short-term memory. TC-LSTM unit is incorporated into an auto-encoder-decoder so that the end-to-end model can handle irregular sampling intervals of longitudinal GCIPL thickness map sequences and capture both spatial and temporal correlations. Experiments show the superiority of the proposed model over the traditional method.

Original languageEnglish (US)
Title of host publicationISBI 2020 - 2020 IEEE International Symposium on Biomedical Imaging
PublisherIEEE Computer Society
Pages1574-1578
Number of pages5
ISBN (Electronic)9781538693308
DOIs
StatePublished - Apr 2020
Event17th IEEE International Symposium on Biomedical Imaging, ISBI 2020 - Iowa City, United States
Duration: Apr 3 2020Apr 7 2020

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2020-April
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Conference

Conference17th IEEE International Symposium on Biomedical Imaging, ISBI 2020
CountryUnited States
CityIowa City
Period4/3/204/7/20

Keywords

  • GCIPL
  • LSTM
  • irregularly sampled data

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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