Difficulty-aware coupled contour regression network with IoU loss for efficient IVUS delineation

The lumen and external elastic lamina contour delineation is crucial for quantitative analyses of intravascular ultrasound (IVUS) images. However, the various artifacts in IVUS images pose substantial challenges for accurate delineation. Existing mask-based methods often produce anatomically implausible contours in artifact-affected images, while contour-based methods suffer from the over-smooth problem within the artifact regions. In this paper, we directly regress the contour pairs instead of mask-based segmentation. A coupled contour representation is adopted to learn a low-dimensional contour signature space, where the embedded anatomical prior enables the model to avoid producing unreasonable results. Further, a PIoU loss is proposed to capture the overall shape of the contour points and maximize the similarity between the regressed contours and manually delineated contours with various irregular shapes, alleviating the over-smooth problem. For the images with severe artifacts, a difficulty-aware training strategy is designed for contour regression, which gradually guides the model focus on hard samples and improves contour localization accuracy. We evaluate the proposed framework on a large IVUS dataset, consisting of 7204 frames from 185 pullbacks. The mean Dice similarity coefficients of the method for the lumen and external elastic lamina are 0.951 and 0.967, which significantly outperforms other state-of-the-art (SOTA) models. All regressed contours in the test images are anatomically plausible. On the public IVUS-2011 dataset, the proposed method attains comparable performance to the SOTA models with the highest processing speed at 100 fps. The code is available at https://github.com/SMU-MedicalVision/ContourRegression.