A Label‐Efficient Framework for Automated Sinonasal CT Segmentation in Image‐Guided Surgery

Segmentation, the partitioning of patient imaging into multiple, labeled segments, has several potential clinical benefits but when performed manually is tedious and resource intensive. Automated deep learning (DL)-based segmentation methods can streamline the process. The objective of this study was to evaluate a label-efficient DL pipeline that requires only a small number of annotated scans for semantic segmentation of sinonasal structures in CT scans.