Simulated Scribble Generation for Interactive Segmentation Refinement for Head and Neck Radiotherapy

Abstract

Accurate segmentation of organs at risk in head-and-neck radiotherapy is important for limiting radiation-induced damage to healthy tissue, yet fully automatic segmentation methods still require manual correction in difficult cases. Interactive refinement offers a practical alternative, but many refinement systems are trained with simulated point annotations while being used in practice with human scribbles. This creates a train-test mismatch in the user-guidance channels, which we study in this thesis as annotation-shift. To address this problem, we propose a training pipeline for interactive segmentation refinement based on simulated human-like scribbles, combined with annotation-aware supervision, distance-map encodings, and explicit manipulation of the initial segmentation. The method is evaluated on head-and-neck CT data for parotid gland segmentation and compared with both a standard point-based baseline and a balanced point-based baseline. The results show that outperforms the original point-based refinement strategy, with the clearest gains appearing in the local evaluation near the annotated region. At the same time, the smaller gap between scribbles and balanced points indicates that refinement quality depends not only on annotation geometry, but also on the amount and spatial distribution of corrective information. These findings provide evidence that annotation-shift affects interactive refinement in interactive medical image segmentation and that simulated scribbles provide a useful step toward refinement systems that are better aligned with real clinical annotation practice.