Dual-Prompt-Enhanced Multiorgan Segmentation Model for Total-Body PET Images

Multiorgan segmentation in total-body positron emission tomography (PET) images is crucial for accurately locating abnormalities and assisting in the observation of corresponding metabolic regions in the human body. Despite the emergence of numerous advanced methods in the field of multiorgan segmentation in recent years, available PET image segmentation techniques remain relatively limited. The complexity and variability of textures in PET images, the varying visibility and contrast of organs due to different metabolic activities, and the challenges posed by blurred organ boundaries in PET images all contribute to the increased difficulty of multiorgan segmentation. In this article, we propose the dual-prompt enhanced multiorgan segmentation model (DPESeg) for total-body PET image segmentation. Our approach focuses on enhancing the model’s ability to perceive organ thresholds and shapes by introducing textual and disentangled organ features, thereby improving segmentation accuracy. We validate our model on a dataset of total-body PET images obtained from 110 patients. Both visual and quantitative results demonstrate that DPESeg performs well in the multiorgan segmentation task, with a 2.02% improvement in the Dice coefficient and a 1.90% improvement in the Jaccard index compared to the best-performing comparison algorithm.