STRUCTURAL CONSTRAINED VIRTUAL HISTOLOGY STAINING FOR HUMAN CORONARY IMAGING USING DEEP LEARNING

被引：2

作者：

Li, Xueshen ^{[1
]}

Liu, Hongshan ^{[1
]}

Song, Xiaoyu ^{[3
]}

Brott, Brigitta C. ^{[2
]}

Litovsky, Silvio H. ^{[2
]}

Gan, Yu ^{[1
]}

机构：

[1] Stevens Insitute Technol, Dept Biomed Engn, Hoboken, NJ 07030 USA

[2] Univ Alabama Birmingham, Sch Med, Birmingham, AL USA

[3] Icahn Sch Med Mt Sinai, New York, NY 10029 USA

来源：

2023 IEEE 20TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING, ISBI | 2023年

基金：

美国国家科学基金会;

关键词：

Virtual histology; Coronary artery disease; Optical coherence tomography; Deep learning; OPTICAL COHERENCE TOMOGRAPHY;

D O I：

10.1109/ISBI53787.2023.10230480

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

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