Structural Constrained Virtual Histology Staining for Human Coronary Imaging Using Deep Learning

Xueshen Li; Hongshan Liu; Xiaoyu Song; Brigitta C. Brott; Silvio H. Litovsky; Yu Gan

doi:10.1109/ISBI53787.2023.10230480

Structural Constrained Virtual Histology Staining for Human Coronary Imaging Using Deep Learning

Xueshen Li
, Hongshan Liu
, Xiaoyu Song
, Brigitta C. Brott
, Silvio H. Litovsky
, Yu Gan

Department of Biomedical Engineering

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

6 Scopus citations

Abstract

Histopathological analysis is crucial in artery characterization for coronary artery disease (CAD). However, histology requires an invasive and time-consuming process. In this paper, we propose to generate virtual histology staining using Optical Coherence Tomography (OCT) images to enable real-time histological visualization. We develop a deep learning network, namely Coronary-GAN, to transfer coronary OCT images to virtual histology images. With a special consideration on the structural constraints in coronary OCT images, our method achieves better image generation performance than the conventional GAN-based method. The experimental results indicate that Coronary-GAN generates virtual histology images that are similar to real histology images, revealing the human coronary layers.

Original language	English
Title of host publication	2023 IEEE International Symposium on Biomedical Imaging, ISBI 2023
ISBN (Electronic)	9781665473583
DOIs	https://doi.org/10.1109/ISBI53787.2023.10230480
State	Published - 2023
Event	20th IEEE International Symposium on Biomedical Imaging, ISBI 2023 - Cartagena, Colombia Duration: 18 Apr 2023 → 21 Apr 2023

Publication series

Name	Proceedings - International Symposium on Biomedical Imaging
Volume	2023-April
ISSN (Print)	1945-7928
ISSN (Electronic)	1945-8452

Conference

Conference	20th IEEE International Symposium on Biomedical Imaging, ISBI 2023
Country/Territory	Colombia
City	Cartagena
Period	18/04/23 → 21/04/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Coronary artery disease
Deep learning
Optical coherence tomography
Virtual histology

Access to Document

10.1109/ISBI53787.2023.10230480

Cite this

Li, X., Liu, H., Song, X., Brott, B. C., Litovsky, S. H., & Gan, Y. (2023). Structural Constrained Virtual Histology Staining for Human Coronary Imaging Using Deep Learning. In 2023 IEEE International Symposium on Biomedical Imaging, ISBI 2023 (Proceedings - International Symposium on Biomedical Imaging; Vol. 2023-April). https://doi.org/10.1109/ISBI53787.2023.10230480

@inproceedings{1c3cc497e53a4e1a9ad05fa5aea2aba5,

title = "Structural Constrained Virtual Histology Staining for Human Coronary Imaging Using Deep Learning",

abstract = "Histopathological analysis is crucial in artery characterization for coronary artery disease (CAD). However, histology requires an invasive and time-consuming process. In this paper, we propose to generate virtual histology staining using Optical Coherence Tomography (OCT) images to enable real-time histological visualization. We develop a deep learning network, namely Coronary-GAN, to transfer coronary OCT images to virtual histology images. With a special consideration on the structural constraints in coronary OCT images, our method achieves better image generation performance than the conventional GAN-based method. The experimental results indicate that Coronary-GAN generates virtual histology images that are similar to real histology images, revealing the human coronary layers.",

keywords = "Coronary artery disease, Deep learning, Optical coherence tomography, Virtual histology",

author = "Xueshen Li and Hongshan Liu and Xiaoyu Song and Brott, \{Brigitta C.\} and Litovsky, \{Silvio H.\} and Yu Gan",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 20th IEEE International Symposium on Biomedical Imaging, ISBI 2023 ; Conference date: 18-04-2023 Through 21-04-2023",

year = "2023",

doi = "10.1109/ISBI53787.2023.10230480",

language = "English",

series = "Proceedings - International Symposium on Biomedical Imaging",

booktitle = "2023 IEEE International Symposium on Biomedical Imaging, ISBI 2023",

}

Li, X, Liu, H, Song, X, Brott, BC, Litovsky, SH & Gan, Y 2023, Structural Constrained Virtual Histology Staining for Human Coronary Imaging Using Deep Learning. in 2023 IEEE International Symposium on Biomedical Imaging, ISBI 2023. Proceedings - International Symposium on Biomedical Imaging, vol. 2023-April, 20th IEEE International Symposium on Biomedical Imaging, ISBI 2023, Cartagena, Colombia, 18/04/23. https://doi.org/10.1109/ISBI53787.2023.10230480

Structural Constrained Virtual Histology Staining for Human Coronary Imaging Using Deep Learning. / Li, Xueshen; Liu, Hongshan; Song, Xiaoyu et al.
2023 IEEE International Symposium on Biomedical Imaging, ISBI 2023. 2023. (Proceedings - International Symposium on Biomedical Imaging; Vol. 2023-April).

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

TY - GEN

T1 - Structural Constrained Virtual Histology Staining for Human Coronary Imaging Using Deep Learning

AU - Li, Xueshen

AU - Liu, Hongshan

AU - Song, Xiaoyu

AU - Brott, Brigitta C.

AU - Litovsky, Silvio H.

AU - Gan, Yu

PY - 2023

Y1 - 2023

N2 - Histopathological analysis is crucial in artery characterization for coronary artery disease (CAD). However, histology requires an invasive and time-consuming process. In this paper, we propose to generate virtual histology staining using Optical Coherence Tomography (OCT) images to enable real-time histological visualization. We develop a deep learning network, namely Coronary-GAN, to transfer coronary OCT images to virtual histology images. With a special consideration on the structural constraints in coronary OCT images, our method achieves better image generation performance than the conventional GAN-based method. The experimental results indicate that Coronary-GAN generates virtual histology images that are similar to real histology images, revealing the human coronary layers.

AB - Histopathological analysis is crucial in artery characterization for coronary artery disease (CAD). However, histology requires an invasive and time-consuming process. In this paper, we propose to generate virtual histology staining using Optical Coherence Tomography (OCT) images to enable real-time histological visualization. We develop a deep learning network, namely Coronary-GAN, to transfer coronary OCT images to virtual histology images. With a special consideration on the structural constraints in coronary OCT images, our method achieves better image generation performance than the conventional GAN-based method. The experimental results indicate that Coronary-GAN generates virtual histology images that are similar to real histology images, revealing the human coronary layers.

KW - Coronary artery disease

KW - Deep learning

KW - Optical coherence tomography

KW - Virtual histology

UR - https://www.scopus.com/pages/publications/85172106938

UR - https://www.scopus.com/pages/publications/85172106938#tab=citedBy

U2 - 10.1109/ISBI53787.2023.10230480

DO - 10.1109/ISBI53787.2023.10230480

M3 - Conference contribution

AN - SCOPUS:85172106938

T3 - Proceedings - International Symposium on Biomedical Imaging

BT - 2023 IEEE International Symposium on Biomedical Imaging, ISBI 2023

T2 - 20th IEEE International Symposium on Biomedical Imaging, ISBI 2023

Y2 - 18 April 2023 through 21 April 2023

ER -

Structural Constrained Virtual Histology Staining for Human Coronary Imaging Using Deep Learning

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Cite this