Few Slices Suffice: Multi-faceted Consistency Learning with Active Cross-Annotation for Barely-Supervised 3D Medical Image Segmentation

Deep learning-based 3D medical image segmentation typically demands extensive densely labeled data. Yet, voxel-wise annotation is laborious and costly to obtain. Cross-annotation, which involves annotating only a few slices from different orientations, has recently become an attractive strategy for labeling 3D images. Compared to previous weak labeling methods like bounding boxes and scribbles, it can efficiently preserve the 3D object’s shape and precise boundaries. However, learning from such sparse supervision signals (aka. barely supervised learning (BSL)) still poses great challenges including less fine-grained object perception, less compact class features and inferior generalizability. To this end, we present a Multi-Faceted ConSistency (MF-ConS) learning framework for the BSL scenario. Our approach starts with an active cross-annotation strategy that requires only three orthogonal labeled slices per scan, optimizing the usage of limited annotation budget through a human-in-the-loop process. Building on the popular teacher-student model, MF-ConS is equipped with three types of consistency regularization to tackle the aforementioned challenges of BSL: (i) neighbor-informed object prediction consistency, which improves fine-grained object perception by encouraging the student model to infer complete segmentation from partial visual cues; (ii) non-parametric prototype-driven consistency for more discriminative and compact intra-class features; (iii) a stability constraint under mild perturbations to enhance model’s robustness. Our method is evaluated on the task of brain tumor segmentation from T2-FLAIR MRI and the promising results show the superiority of our approach over relevant state-of-the-art methods.