2D Versus 3D Diffusion for In Silico Training of Interventional X-ray AI Models
The ability to synthesize realistic X-ray images has catalyzed the development of AI models for X-ray image-guided procedures, which otherwise suffer from a lack of available annotated data. Prior work has demonstrated the effectiveness of mechanistic simulation of digitally reconstructed radiographs (DRRs) as a training data source for a myriad of tasks, including segmentation and anatomical landmark detection, with comparable or superior performance to real data training. However, mechanistic DRR synthesis still relies on the availability of annotated high-resolution anatomical models. Deriving these from CT images of real patients or specimens imposes an undesirable bottleneck on data ...
| Authors | Sampath Rapuri, Jeremy Ko, Benjamin D. Killeen et al. |
|---|---|
| Primary category | eess.IV |
| Published | 19 June 2026 |
| Last updated at source | 19 June 2026 |
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