3D spine reconstruction from a single radiograph based on GANs.

Abstract

The 3D spinal model plays a crucial role in the assessment and treatment decision of adolescent idiopathic scoliosis. The complex 3D shape of the spine cannot be fully captured by a single radiograph. A 3D spine reconstruction framework is developed in this study. First, a dual-training strategy for Generative Adversarial Networks (GANs) is proposed, which generates high-quality 3D spinal structures. Second, an adaptive scale-agnostic attention mechanism is integrated to establish cross-layer feature correlations and dynamically allocate weights. This mechanism ensures the preservation of the crucial information across all scales throughout the feature extraction process. The proposed method has been validated on 49 cases of scoliosis. Experiments show that surface overlap and volume Dice coefficient are 0.92 and 0.94, respectively. Compared with the state-of-the-art methods, the proposed method reduces the average surface distance by 0.16 mm. The results demonstrate its effectiveness in reconstructing the 3D spine from a single radiograph.