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
N1 - Publisher Copyright:
© 2023 IEEE.
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 - http://www.scopus.com/inward/record.url?scp=85172106938&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85172106938&partnerID=8YFLogxK
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 -