Abstract Catheter-based coronary intervention is an effective treatment for acute coronary syndrome. However, calcified plaques pose significant challenges within these procedures, as they complicate stent deployment and passage of devices. This study presents novel research for calcium detection in intravascular ultrasound data dealing with weak labeling. Encoding-decoding network architecture is adapted to predict the angle-wise existence of calcification in IVUS data, enabling the interpretation of predictive behavior by observing the attention map at the penultimate layer. We explore several practical factors, including the training of five candidate models and their average ensembling, enhancing dataset diversity, and optimizing filter size for post-processing. The algorithm is developed using stored DICOM data from 42 patients. Employing a 5-fold cross-validation training strategy, we achieve an average accuracy of 0.90 and a Dice score ranging of 0.88 in testing performance. The attention map shows that the trained model learned to use image features to support its decisions, resembling the manner of trained IVUS experts. Comparing the computed calcium scores to the ground truth confirms that using average resembling followed by post-processing with a filter size of 61 A-lines and 13 frames yields the best performance. The window is surprisingly larger than what is typically used in the literature, likely due to its high noise ratio in IVUS images. The detection and quantification pipeline holds promise for accelerating clinical research, particularly in elucidating the impact of calcified plaques on intervention procedures and prognosis. It may also serve as a powerful tool to assist in decision-making during interventions.
Liu et al. (Mon,) studied this question.