Beyond Point Annotation: A Weakly Supervised Network Guided by Multi-Level Labels Generated from Four-Point Annotation for Thyroid Nodule Segmentation in Ultrasound Image

Abstract

Weakly supervised methods typically guided the pixel-wise training by comparing the predictions to single-level labels containing diverse segmentation-related information at once, but struggled to represent subtle feature differences between nodule and background regions and confused incorrect information, resulting in under-fitting or over-fitting in the segmentation predictions. This work proposes a weakly supervised network that generates multi-level labels from four-point annotation to refine diverse constraints for delicate nodule segmentation. The Distance-Similarity Fusion Prior referring to the points annotations filters out information irrelevant to nodules. The bounding box and pure foreground/background labels, generated from the point annotation, guarantee the rationality of the prediction in the arrangement of target localization and the spatial distribution of target/background regions, respectively. Our proposed network outperforms existing weakly supervised methods on two public datasets with respect to accuracy and robustness, improving the applicability of deep-learning-based segmentation in the clinical practice of thyroid nodule diagnosis.