X2Shape: CT-free 3D multi-organ reconstruction with biplanar X-rays.

Reconstructing three-dimensional (3D) anatomy from routine X-ray imaging remains a long-standing challenge, promising high accessibility and minimal radiation exposure compared to computed tomography (CT). We propose X2Shape, a deep learning framework that enables direct 3D multi-organ reconstruction from orthogonal biplanar X-rays, without requiring CT priors. X2Shape combines a geometry-aware volumetric backprojection with a cross-view fusion module based on state-space modeling, achieving accurate 2D-to-3D mapping and robust integration of complementary views. To overcome the scarcity of paired training data, we devise a hybrid deformation-based augmentation strategy that generates anatomically diverse, realistic samples, markedly improving model generalization. Across two challenging thoracic benchmarks, X2Shape substantially outperforms existing methods, reaching Dice scores of 88.98% and 75.62% on TotalSegmentator-Subset and LCTSC datasets, respectively. Beyond accuracy, it demonstrates strong cross-dataset generalization, reconstructing diverse organ structures with efficiency and robustness. By eliminating dependence on CT while preserving volumetric fidelity, X2Shape establishes a scalable paradigm for low-cost, low-radiation 3D imaging, with potential to broaden clinical access to personalized diagnostics, surgical planning, and image-guided interventions. The code is available at https://github.com/pzhhhhh2263/X2Shape.