Accurate analysis and modeling of renal functions require a precise segmentation of the renal blood vessels. Micro-CT scans provide image data at higher resolutions, making more small vessels near the renal cortex visible. Although deep-learning-based methods have shown state-of-the-art performance in automatic blood vessel segmentations, they require a large amount of labeled training data. However, voxel-wise labeling in micro-CT scans is extremely time-consuming given the huge volume sizes. To mitigate the problem, we simulate synthetic renal vascular trees physiologically while generating corresponding scans of the simulated trees by training a generative model on unlabeled scans. This enables the generative model to learn the mapping implicitly without the need for explicit functions to emulate the image acquisition process. We further propose an additional segmentation branch over the generative model trained on the generated scans. We demonstrate that the model can directly segment blood vessels on real scans and validate our method on both 3D micro-CT scans of rat kidneys and a proof-of-concept experiment on 2D retinal images. Code and 3D results are available at https://github.com/miccai2023anony/RenalVesselSeg

翻译：肾脏功能的精确分析与建模需要对其血管进行精准分割。显微CT扫描提供了更高分辨率的图像数据，使肾脏皮质附近更多小血管得以可见。尽管基于深度学习方法在自动血管分割中展现出先进性能，但它们需要大量标记训练数据。然而，考虑到微CT扫描的巨大体积规模，逐体素标记极其耗时。为缓解这一问题，我们通过生理学方法模拟合成肾血管树，并利用无标记扫描训练生成模型生成模拟树对应的扫描图像。这使得生成模型能够隐式学习映射关系，无需显式函数模拟图像采集过程。我们进一步提出在生成模型基础上增加一个分割分支，该分支基于生成的扫描图像进行训练。实验证明，该模型可直接对真实扫描图像中的血管进行分割，并在大鼠肾脏三维微CT扫描及二维视网膜图像的原理验证实验中验证了该方法。代码和三维结果见 https://github.com/miccai2023anony/RenalVesselSeg