Knowledge-Guided 3D CT Generation: A Conditioning-Centric Taxonomy
Controllable generation guided by external knowledge is a key requirement in modern generative deep learning applications, enabling the synthesis of samples with explicit constraints on semantic content, structural properties, and variability. In 3D Computed Tomography (CT), such control is essential for clinical applications, including data augmentation, privacy-preserving data sharing, and the simulation of specific anatomical or pathological scenarios. While research on conditional 3D CT generation has expanded rapidly, the diversity of existing approaches makes systematic comparison difficult and obscures fundamental design choices. In this survey, we propose a conditioning-centric ta...
| Authors | Francesca Pia Panaccione, Eugenio Lomurno, Matteo Matteucci |
|---|---|
| Primary category | eess.IV |
| Published | 7 August 2026 |
| Last updated at source | 7 August 2026 |
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