Deep learning-enhanced contour interpolation techniques for 3D carotid vessel wall segmentation

When studying human vessels using the contour interpolation method, there is a problem of insufficient data for training neural networks for automatic segmentation of the carotid artery wall. In this paper, automated methods of contour interpolation are proposed to expand the datasets, which allows for improved segmentation of vessel walls and atherosclerotic plaques. In this study, the performance of various interpolation methods is compared with the traditional nearest neighboring technique. A theoretical description and comparative evaluation of Linear, Polar, and Spline interpolation are presented. Quantitative metrics, including the Dice Similarity Coefficient, area and index differences, and normalized Hausdorff distances, are used to evaluate the performance of the methods. Performance evaluations are performed on various vessel morphologies for both the lumen and the outer wall boundaries. The study showed that Linear interpolation achieves better geometric performance (Cohen’s Kappa 0.92) and improved neural network performance (Score 0.86) compared to the State-of-the-Art model. The proposed interpolation methods consistently outperform nearest neighbor interpolation. Polar and spline methods are effective in generating anatomically plausible contours with improved smoothness and continuity, eliminating transition artifacts between slices. Statistical analysis confirmed good agreement and reduced variation of these methods. The results of the study are useful for the development of automated tools for assessing atherosclerotic plaque in carotid arteries, which is important for stroke prevention. Implementation of improved interpolation methods into clinical imaging workflows can significantly improve the reliability, accuracy, and clinical utility of vessel wall segmentation.

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