Anatomically constrained CT image translation for heterogeneous blood vessel segmentation
- Philips Research Paris（飞利浦巴黎研究院）
- Institut Imagine, Université Paris Cité（巴黎西岱大学想象研究所）
- Université Paris Cité（巴黎西岱大学）
- Hôpital Necker Enfants-Malades, APHP（巴黎公共援助医院集团内克儿童病院）
- Sorbonne Université（索邦大学）
- CNRS, LIP6（法国国家科学研究中心LIP6实验室）
- Technology Innovation Institute（技术创新研究所）
机构由 AI 辅助整理，请以论文原文为准。
英文摘要：
Anatomical structures such as blood vessels in contrast-enhanced CT (ceCT) images can be challenging to segment due to the variability in contrast medium diffusion. The combined use of ceCT and contrast-free (CT) CT images can improve the segmentation performances, but at the cost of a double radiation exposure. To limit the radiation dose, generative models could be used to synthesize one modality, instead of acquiring it. The CycleGAN approach has recently attracted particular attention because it alleviates the need for paired data that are difficult to obtain. Despite the great performances demonstrated in the literature, limitations still remain when dealing with 3D volumes generated slice by slice from unpaired datasets with different fields of view. We present an extension of CycleGAN to generate high fidelity images, with good structural consistency, in this context. We leverage anatomical constraints and automatic region of interest selection by adapting the Self-Supervised Body Regressor. These constraints enforce anatomical consistency and allow feeding anatomically-paired input images to the algorithm. Results show qualitative and quantitative improvements, compared to stateof-the-art methods, on the translation task between ceCT and CT images (and vice versa).
