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

doi:10.1109/ISBI45749.2020.9098614

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 proceeding › Conference 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 language	English (US)
Title of host publication	ISBI 2020 - 2020 IEEE International Symposium on Biomedical Imaging
Publisher	IEEE Computer Society
Pages	1574-1578
Number of pages	5
ISBN (Electronic)	9781538693308
DOIs	https://doi.org/10.1109/ISBI45749.2020.9098614
State	Published - Apr 2020
Event	17th IEEE International Symposium on Biomedical Imaging, ISBI 2020 - Iowa City, United States Duration: Apr 3 2020 → Apr 7 2020

Publication series

Name	Proceedings - International Symposium on Biomedical Imaging
Volume	2020-April
ISSN (Print)	1945-7928
ISSN (Electronic)	1945-8452

Conference

Conference	17th IEEE International Symposium on Biomedical Imaging, ISBI 2020
Country/Territory	United States
City	Iowa City
Period	4/3/20 → 4/7/20

Keywords

GCIPL
LSTM
irregularly sampled data

ASJC Scopus subject areas

Biomedical Engineering
Radiology Nuclear Medicine and imaging

Access to Document

10.1109/ISBI45749.2020.9098614

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). Article 9098614 (Proceedings - International Symposium on Biomedical Imaging; Vol. 2020-April). IEEE Computer Society. https://doi.org/10.1109/ISBI45749.2020.9098614

Macular GCIPL Thickness Map Prediction via Time-Aware Convolutional LSTM. / Chen, Zhiqi; Wang, Yao; Wollstein, Gadi et al.
ISBI 2020 - 2020 IEEE International Symposium on Biomedical Imaging. IEEE Computer Society, 2020. p. 1574-1578 9098614 (Proceedings - International Symposium on Biomedical Imaging; Vol. 2020-April).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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., 9098614, Proceedings - International Symposium on Biomedical Imaging, vol. 2020-April, IEEE Computer Society, pp. 1574-1578, 17th IEEE International Symposium on Biomedical Imaging, ISBI 2020, Iowa City, United States, 4/3/20. https://doi.org/10.1109/ISBI45749.2020.9098614

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

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title = "Macular GCIPL Thickness Map Prediction via Time-Aware Convolutional LSTM",

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.",

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AB - 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.

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Macular GCIPL Thickness Map Prediction via Time-Aware Convolutional LSTM

Abstract

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Keywords

ASJC Scopus subject areas

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