Abstract

Soft-label assignments have emerged as prominent strategies in training dense prediction problems, such as image segmentation. These approaches mitigate the limitations of hard labels, such as inter-class relationships in the data and spatial relationships between a given pixel and its neighbors. Nevertheless, most existing methods rely only on ground-truth masks and ignore the underlying image context associated with each label. For instance, image intensities convey information that could potentially clear ambiguities in the annotation. This paper, therefore, proposes a Geodesic Label Smoothing (GeoLS) approach that incorporates image intensity information within the soft labeling process. Specifically, we leverage the geodesic distance transform to capture the intensity variations between pixels. The generated maps geodesically modify the hard labels to obtain new intensity-based soft labels. The resulting geodesic soft labels better model spatial and class-wise relationships as they capture the variations of image gradients across classes and anatomy. The benefits of our intensity-based geodesic soft labels are assessed on three diverse sets of publicly accessible segmentation datasets. Our experimental results show that the proposed method consistently improves the segmentation accuracy compared to state-of-the-art soft-labeling techniques in terms of the Dice similarity and Hausdorff distance.