Point-annotation supervision for robust 3D pulmonary infection segmentation by CT-based cascading deep learning

Infected region segmentation is crucial for pulmonary infection diagnosis, severity assessment, and monitoring treatment progression. High-performance segmentation methods rely heavily on fully annotated, large-scale training datasets. However, manual labeling for pulmonary infections demands substantial investments of time and labor. While weakly supervised learning can greatly reduce annotation efforts, previous developments have focused mainly on natural or medical images with distinct boundaries and consistent textures. These approaches is not applicable to pulmonary infection segmentation, which should contend with high topological and intensity variations, irregular and ambiguous boundaries, and poor contrast in 3D contexts. In this study, we propose a cascading point-annotation framework to segment pulmonary infections, enabling optimization on larger datasets and superior performance on external data. Via comparing the representation of annotated points and unlabeled voxels, as well as establishing global uncertainty, we develop two regularization strategies to constrain the network to a more holistic lesion pattern understanding under sparse annotations. We further encompass an enhancement module to improve global anatomical perception and adaptability to spatial anisotropy, alongside a texture-aware variational module to determine more regionally consistent boundaries based on common textures of infection. Experiments on a large dataset of 1,072 CT volumes demonstrate our method outperforming state-of-the-art weakly-supervised approaches by approximately 3%–6% in dice score and is comparable to fully-supervised methods on external datasets. Moreover, our approach demonstrates robust performance even when applied to an unseen infection subtype, Mycoplasma pneumoniae, which was not included in the training datasets. These results collectively underscore rapid and promising applicability for emerging pulmonary infections.