Semi-supervised vessel wall detection and segmentation from 3D femoral MR images

Abstract

Vessel wall volume (VWV) for the femoral arteries is a sensitive indicator of coexistent generalized atherosclerosis. Measuring VWV requires the segmentation of lumen and outer wall boundaries from 3D MR images. The main challenge for vessel wall segmentation is the small size of femoral artery in a 3D MR image and the existence of objects mimicking arteries. Besides, due to the long span of the femoral artery and time-consuming manual segmentation, a large number of image slices are not manually segmented, and therefore, cannot be used to train fully supervised methods. We proposed a semi-supervised end-to-end artery localization and segmentation model that improves segmentation performance through the use of axial image slices that are not manually segmented (unlabeled slices). The method localizes femoral arteries with bounding boxes and performs segmentation over the selected regions. A mean teacher framework was trained to generate high-quality segmentation for unlabeled slices, serving as pseudo-labels to improve the student model's performance in arterial detection and vessel wall segmentation. A new continuity score was developed to further improve the quality of the vessel wall segmentation on unlabeled image slices. Our experiments show that the semi-supervised approach and the proposed continuity score independently improve the femoral vessel wall segmentation.