Department of Software and Information Technology Engineering, École de Technologie Supérieure, 1100 Notre-Dame, Montréal, QC, H3C 1K3, Canada.
Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University Medical Center, 2424 Erwin Road, Durham, NC, 27705, USA.
Int J Comput Assist Radiol Surg. 2023 Dec;18(12):2329-2338. doi: 10.1007/s11548-023-02982-3. Epub 2023 Jun 19.
Medical image analysis suffers from a sparsity of annotated data necessary in learning-based models. Cardiorespiratory simulators have been developed to counter the lack of data. However, the resulting data often lack realism. Hence, the proposed method aims to synthesize realistic and fully customizable angiograms of coronary arteries for the training of learning-based biomedical tasks, for cardiologists performing interventions, and for cardiologist trainees.
3D models of coronary arteries are generated with a fully customizable realistic cardiorespiratory simulator. The transfer of X-ray angiography style to simulator-generated images is performed using a new vessel-specific adaptation of the CycleGAN model. The CycleGAN model is paired with a vesselness-based loss function that is designed as a vessel-specific structural integrity constraint.
Validation is performed both on the style and on the preservation of the shape of the arteries of the images. The results show a PSNR of 14.125, an SSIM of 0.898, and an overlapping of 89.5% using the Dice coefficient.
We proposed a novel fluoroscopy-based style transfer method for the enhancement of the realism of simulated coronary artery angiograms. The results show that the proposed model is capable of accurately transferring the style of X-ray angiograms to the simulations while keeping the integrity of the structures of interest (i.e., the topology of the coronary arteries).
基于学习的模型在学习过程中需要大量的标注数据，但医学图像分析的数据通常较为缺乏。为此，开发了心肺模拟器来解决数据不足的问题。然而，由此产生的数据通常缺乏真实感。因此，该方法旨在为基于学习的生物医学任务的训练、介入心脏病专家的操作以及心脏病专家的培训，生成逼真且完全可定制的冠状动脉血管造影图像。
使用完全可定制的真实心肺模拟器生成冠状动脉的 3D 模型。使用 CycleGAN 模型的新血管特异性自适应方法将 X 射线血管造影样式转换为模拟器生成的图像。CycleGAN 模型与基于血管性的损失函数配对，该损失函数旨在作为血管特异性结构完整性约束。
对图像的样式和血管形状的保持进行了验证。结果表明，使用 Dice 系数，PSNR 为 14.125，SSIM 为 0.898，重叠率为 89.5%。
我们提出了一种新颖的基于荧光透视术的样式转换方法，用于增强模拟冠状动脉血管造影的真实感。结果表明，所提出的模型能够准确地将 X 射线血管造影的样式转换为模拟图像，同时保持感兴趣结构（即冠状动脉的拓扑结构）的完整性。
