Bifurcation detection in intravascular optical coherence tomography using vision transformer based deep learning.
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Abstract
Bifurcation detection in intravascular optical coherence tomography (IVOCT) images plays a significant role in guiding optimal revascularization strategies for percutaneous coronary intervention (PCI). We propose a bifurcation detection method using vision transformer (ViT) based deep learning in IVOCT.Instead of relying on lumen segmentation, the proposed method identifies the bifurcation image using a ViT-based classification model and then estimate bifurcation ostium points by a ViT-based landmark detection model.
Main results.By processing 8640 clinical images, the Accuracy and F1-score of bifurcation identification by the proposed ViT-based model are 2.54% and 16.08% higher than that of traditional non-deep learning methods, are similar to the best performance of convolutional neural networks (CNNs) based methods, respectively. The ostium distance error of the ViT-based model is 0.305mm, which is reduced 68.5% compared with the traditional non-deep learning method and reduced 24.81% compared with the best performance of CNNs based methods. The results also show that the proposed ViT-based method achieves the highest success detection rate (SDR) are 11.3% and 29.2% higher than the non-deep learning method, and 4.6% and 2.5% higher than the best performance of CNNs based methods when the distance threshold is 0.1 mm and 0.2 mm, respectively.The proposed ViT-based method enhances the performance of bifurcation detection of IVOCT images, which maintains a high correlation and consistency between the automatic detection results and the expert manual results. It is of great significance in guiding the selection of PCI treatment strategies.
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