Background:
Accurate segmentation of the coronary arteries with intravascular ultrasound (IVUS) is important to optimize coronary stent implantation. Recently, deep learning (DL) methods have been proposed to develop automatic IVUS segmentation. However, most of those have been limited to the lumen and vessel segmentation, not applied to segmenting stent dimension. Hence, this study aimed to develop a DL method for automatic IVUS segmentation of stent area in addition to lumen and vessel area.
Methods:
This study included a total of 45449 images from 1576 (40- or 45-MHz) IVUS pullback runs. The datasets were randomly split into training, validation, and test datasets (0.7:0.15:0.15). After constructing the convolutional neural network to segment IVUS images using the training and validation datasets, we evaluated the performance through the independent test dataset.
Results:
The DL-based segmentation correlated well with the expert-analysed segmentation with a mean intersection over union of 0.84, the correlation coefficient of 0.99, 0.98, and 0.99 and the mean difference of -0.04±0.05, -0.20±0.11 and -0.12±0.06 mm^2 for lumen, vessel and stent area derived from respectively.
Conclusion:
This automated DL-based IVUS segmentation of stent area in conjunction with lumen and vessel area showed good agreement with manual segmentation by experts, supporting the feasibility of artificial-intelligence-assisted IVUS assessment and strategy-making in patients undergoing coronary stent implantation.
High Pressure Assisted Coronary Stent Implantation Accomplished Without Intravascular Ultrasound Guidance and Subsequent Anticoagulation
