Extremely weakly-supervised blood vessel segmentation with
  physiologically based synthesis and domain adaptation
Peidi Xu Olga Sosnovtseva Charlotte Mehlin Sørensen Kenny Erleben Sune Darkner
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