CED-Diff: a clinically-experience-driven diffusion model with differentiated feature extraction for head and neck...

Objective.Automatic segmentation of head and neck tumours from PET/CT images is important for radiotherapy planning and related image-guided workflows, but remains challenging because PET and CT provide complementary yet heterogeneous information. Existing fusion strategies often do not explicitly model metabolic hotspots in PET and anatomical boundary information in CT, which can impair the delineation of small lesions and lesions with indistinct boundaries.Approach.We propose CED-Diff, a diffusion-based tumour segmentation framework that integrates differentiated feature extraction (DFE) and task-oriented auxiliary supervision (TAS). DFE explicitly models metabolic activity in PET and anatomical boundary information in CT to derive more discriminative features for segmentation, while TAS provides auxiliary constraints to improve feature learning.Main results.On the HeadNeck dataset, CED-Diff achieved the highest Dice score among the evaluated methods, with a Dice score of 66.28%, along with a Sensitivity of 78.70% and a Precision of 69.08%.Significance.These results suggest that explicit modelling of complementary PET and CT information can improve the accuracy and robustness of automatic head and neck tumour segmentation.