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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  • Cite this

    Chen, Z., Wang, Y., Wollstein, G., De Los Angeles Ramos-Cadena, M., Schuman, J., & Ishikawa, H. (2020). Macular GCIPL Thickness Map Prediction via Time-Aware Convolutional LSTM. In ISBI 2020 - 2020 IEEE International Symposium on Biomedical Imaging (pp. 1574-1578). [9098614] (Proceedings - International Symposium on Biomedical Imaging; Vol. 2020-April). IEEE Computer Society. https://doi.org/10.1109/ISBI45749.2020.9098614