🤖 AI Summary
This work addresses the bottleneck of labor-intensive dense manual annotations in 3D carotid vessel wall segmentation. We propose a fully automatic 3D segmentation framework leveraging only sparse centerline annotations. Methodologically, we introduce a novel bifurcation-axis-perpendicular cross-sectional pseudo-label generation strategy, integrated with centerline-driven transverse slice sampling, adversarial 2D U-Net–based initial segmentation, geometry-aware pseudo-label projection, and end-to-end 3D U-Net optimization. This paradigm efficiently transforms easily obtainable sparse 2D centerline labels into high-fidelity 3D vessel wall segmentations, notably improving accuracy at bifurcations. Evaluated on real carotid imaging data, our method enables precise quantification of 3D biomarkers—including plaque volume—and delivers a clinically deployable, automated solution for carotid stenosis assessment.
📝 Abstract
We propose a novel approach that uses sparse annotations from clinical studies to train a 3D segmentation of the carotid artery wall. We use a centerline annotation to sample perpendicular cross-sections of the carotid artery and use an adversarial 2D network to segment them. These annotations are then transformed into 3D pseudo-labels for training of a 3D convolutional neural network, circumventing the creation of manual 3D masks. For pseudo-label creation in the bifurcation area we propose the use of cross-sections perpendicular to the bifurcation axis and show that this enhances segmentation performance. Different sampling distances had a lesser impact. The proposed method allows for efficient training of 3D segmentation, offering potential improvements in the assessment of carotid artery stenosis and allowing the extraction of 3D biomarkers such as plaque volume.