DpDNet: An Dual-Prompt-Driven Network for Universal PET-CT Segmentation
Journal:
arXiv
Published Date:
Jul 8, 2025
Abstract
PET-CT lesion segmentation is challenging due to noise sensitivity, small and
variable lesion morphology, and interference from physiological high-metabolic
signals. Current mainstream approaches follow the practice of one network
solving the segmentation of multiple cancer lesions by treating all cancers as
a single task. However, this overlooks the unique characteristics of different
cancer types. Considering the specificity and similarity of different cancers
in terms of metastatic patterns, organ preferences, and FDG uptake intensity,
we propose DpDNet, a Dual-Prompt-Driven network that incorporates specific
prompts to capture cancer-specific features and common prompts to retain shared
knowledge. Additionally, to mitigate information forgetting caused by the early
introduction of prompts, prompt-aware heads are employed after the decoder to
adaptively handle multiple segmentation tasks. Experiments on a PET-CT dataset
with four cancer types show that DpDNet outperforms state-of-the-art models.
Finally, based on the segmentation results, we calculated MTV, TLG, and SUVmax
for breast cancer survival analysis. The results suggest that DpDNet has the
potential to serve as a valuable tool for personalized risk stratification,
supporting clinicians in optimizing treatment strategies and improving
outcomes. Code is available at https://github.com/XinglongLiang08/DpDNet.
