Soliman Ahmad, Keuth Ron, Himstedt Marian
Medical Informatics, University of Lübeck, Ratzeburger Allee 160, 23562, Lübeck, Germany.
Faculty of Electrical Engineering and Computer Science, University of Technology Lübeck, Mönkhofer Weg 239, 23909, Lübeck, Germany.
Int J Comput Assist Radiol Surg. 2025 Jun 25. doi: 10.1007/s11548-025-03450-w.
Purpose The limited availability of bronchoscopy images makes image synthesis particularly interesting for training deep learning models. Robust image translation across different domains-virtual bronchoscopy, phantom as well as in vivo and ex vivo image data-is pivotal for clinical applications. Methods This paper proposes BronchoGAN introducing anatomical constraints for image-to-image translation being integrated into a conditional GAN. In particular, we force bronchial orifices to match across input and output images. We further propose to use foundation model-generated depth images as intermediate representation ensuring robustness across a variety of input domains establishing models with substantially less reliance on individual training datasets. Moreover, our intermediate depth image representation allows to easily construct paired image data for training. Results Our experiments showed that input images from different domains (e.g., virtual bronchoscopy, phantoms) can be successfully translated to images mimicking realistic human airway appearance. We demonstrated that anatomical settings (i.e., bronchial orifices) can be robustly preserved with our approach which is shown qualitatively and quantitatively by means of improved FID, SSIM and dice coefficients scores. Our anatomical constraints enabled an improvement in the Dice coefficient of up to 0.43 for synthetic images. Conclusion Through foundation models for intermediate depth representations and bronchial orifice segmentation integrated as anatomical constraints into conditional GANs, we are able to robustly translate images from different bronchoscopy input domains. BronchoGAN allows to incorporate public CT scan data (virtual bronchoscopy) in order to generate large-scale bronchoscopy image datasets with realistic appearance. BronchoGAN enables to bridge the gap of missing public bronchoscopy images.
目的 支气管镜图像的有限可用性使得图像合成对于训练深度学习模型特别有意义。跨不同领域（虚拟支气管镜、体模以及体内和体外图像数据）的稳健图像翻译对于临床应用至关重要。方法 本文提出了BronchoGAN，将用于图像到图像翻译的解剖学约束集成到条件生成对抗网络中。具体而言，我们强制支气管口在输入和输出图像之间匹配。我们还建议使用基础模型生成的深度图像作为中间表示，以确保在各种输入域上的稳健性，从而建立对单个训练数据集依赖大大减少的模型。此外，我们的中间深度图像表示允许轻松构建用于训练的配对图像数据。结果 我们的实验表明，来自不同领域（如虚拟支气管镜、体模）的输入图像可以成功转换为模仿真实人类气道外观的图像。我们证明，通过我们的方法可以稳健地保留解剖学设置（即支气管口），这通过改进的FID、SSIM和骰子系数得分在定性和定量上得到了证明。我们的解剖学约束使合成图像的骰子系数提高了高达0.43。结论 通过将用于中间深度表示的基础模型和作为解剖学约束的支气管口分割集成到条件生成对抗网络中，我们能够稳健地翻译来自不同支气管镜输入域的图像。BronchoGAN允许合并公共CT扫描数据（虚拟支气管镜）以生成具有真实外观的大规模支气管镜图像数据集。BronchoGAN能够弥合公共支气管镜图像缺失的差距。
