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X2Shape: CT-free 3D multi-organ reconstruction with biplanar X-rays
Zhaohong Pan1, Haowei Zhou1, Qi Ren2
1Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong 518055, China; University of Chinese Academy of Sciences, Beijing, 100049, China.
Medical Image Analysis
|April 23, 2026
Summary
X2Shape reconstructs 3D organs from X-rays using deep learning, offering a low-radiation alternative to CT scans. This method enhances accessibility for diagnostics and surgical planning.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Computational Anatomy
Background:
- Reconstructing 3D anatomy from 2D X-rays is challenging but offers lower radiation exposure than CT.
- Existing methods often rely on CT data or lack accuracy and robustness.
Purpose of the Study:
- To develop a deep learning framework (X2Shape) for direct 3D multi-organ reconstruction from biplanar X-rays.
- To overcome data scarcity using a novel augmentation strategy.
- To achieve accurate and robust 3D reconstruction without CT priors.
Main Methods:
- Developed X2Shape, a deep learning framework utilizing geometry-aware volumetric backprojection and a state-space model-based cross-view fusion module.
- Implemented a hybrid deformation-based augmentation strategy for generating diverse training data.
- Validated the framework on two thoracic benchmarks (TotalSegmentator-Subset and LCTSC).
Main Results:
- X2Shape achieved high accuracy, with Dice scores of 88.98% and 75.62% on the tested datasets.
- Demonstrated substantial improvement over existing methods.
- Showcased strong cross-dataset generalization and efficient, robust reconstruction of diverse organ structures.
Conclusions:
- X2Shape provides accurate 3D organ reconstruction from X-rays, eliminating the need for CT.
- This scalable paradigm offers a low-cost, low-radiation solution for 3D imaging.
- Potential applications include personalized diagnostics, surgical planning, and image-guided interventions.
