In Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
BACKGROUND : Accurate and efficient 3-dimension (3D) reconstruction of coronary stents in intravascular imaging of optical coherence tomography (OCT) or intravascular ultrasound (IVUS) is important for optimization of complex percutaneous coronary interventions (PCI). Deep learning has been used to address this technical challenge. However, manual annotation of stent is strenuous, especially for IVUS images. To this end, we aim to explore whether the OCT and IVUS images can assist each other in stent 3D reconstruction when one of them is lack of labeled dataset.
METHODS : We firstly performed cross-modal translation between OCT and IVUS images, where disentangled representation was employed to generate synthetic images with good stent consistency. The reciprocal assistance of OCT and IVUS in stent 3D reconstruction was then conducted by applying unsupervised and semi-supervised learning with the aid of synthetic images. Stent consistency in synthetic images and reciprocal effectiveness in stent 3D reconstruction were quantitatively assessed by F1-Score (FS) on two datasets: OCT-High Definition IVUS (HD IVUS) and OCT-Conventional IVUS (IVUS).
RESULTS : The employment of disentangled representation achieved higher stent consistency in synthetic images (OCT to HD IVUS: FS=0.789 vs 0.684; HD IVUS to OCT: FS=0.766 vs 0.682; OCT to IVUS: FS=0.806 vs 0.664; IVUS to OCT: FS=0.724 vs 0.673). For stent 3D reconstruction, the assistance from synthetic images significantly promoted unsupervised adaptation across modalities (OCT to HD IVUS: FS=0.776 vs 0.109; HD IVUS to OCT: FS=0.826 vs 0.125; OCT to IVUS: FS=0.782 vs 0.068; IVUS to OCT: FS=0.815 vs 0.123), and improved performance in semi-supervised learning, especially when only limited labeled data was available.
CONCLUSION : The intravascular images of OCT and IVUS can provide reciprocal assistance to each other in stent 3D reconstruction by cross-modal translation, where the stent consistency in synthetic images was maintained by disentangled representation.
Wu Peng, Qiao Yuchuan, Chu Miao, Zhang Su, Bai Jingfeng, Gutierrez-Chico Juan Luis, Tu Shengxian
2022-Dec-26
Cross-modal translation, Disentangled representation, Intravascular imaging, Semi-supervised learning, Stent 3D reconstruction, Unsupervised domain adaptation
