Symmetrical Awareness Generation for Pelvic Image Segmentation

The pelvis is a high-incidence region for trauma, and its precise segmentation is vital for clinical diagnosis and treatment. Although deep learning has achieved high precision for pelvis segmentation, it is still confronted with challenges such as the scarcity of medical data and the high cost of annotation. Recently, generative models have offered a viable solution through synthetic data augmentation. Thus, we propose a novel text-conditioned generative framework that simultaneously produces high-fidelity CT images and their corresponding segmentation masks, with a special focus on accurately generating symmetric pelvic structures, including the left hip bone, right hip bone and sacrum. Firstly, we fine-tuned a medical text encoder to transform detailed descriptions into precise generation conditions. Considering the easily overlooked symmetry attribute in pelvic bone images, we introduced a novel coordinate-aware enhancement module that incorporates bone-specific centroid coordinates for symmetrical awareness. Finally, we expanded it to multi-task learning for generation of paired pelvic images and segmentation labels. To test our method, we released a pelvic CT image dataset with textual description (CT-PelvisText) and then transferred it to downstream segmentation using the generative pelvic image. Our experiments demonstrate the reliability of our method, which contributes to accurate pelvic segmentation. This work can also be easily extended to other medical images with symmetry properties, which provides potential for efficient learning in small-sample datasets. Our code and dataset are available at: https://github.com/CurellaSong/TSA_LDM.