Artificial vascular image generation using blood vessel texture maps

Background: Current methods for identifying blood vessels in digital images typically involve training neural networks on pixel-wise annotated data. However, manually outlining whole vessel trees in images tends to be very costly. One approach for reducing the amount of manual annotation is to pre-train networks on artificially generated vessel images. Recent pre-training approaches focus on generating proper artificial geometries for the vessels, while the appearance of the vessels is defined using general statistics of the real samples or generative networks requiring an additional training procedure to be defined. In contrast, we propose a methodology for generating blood vessels with realistic textures extracted directly from manually annotated vessel segments from real samples. The method allows the generation of artificial images having blood vessels with similar geometry and texture to the real samples using only a handful of manually annotated vessels.

Methods: The first step of the method is the manual annotation of the borders of a small vessel segment, which takes only a few seconds. The annotation is then used for creating a reference image containing the texture of the vessel, called a texture map. A procedure is then defined to allow texture maps to be placed on top of any smooth curve using a piecewise linear transformation. Artificial images are then created by generating a set of vessel geometries using Bézier curves and assigning vessel texture maps to the curves.

Results: The method is validated on a fluorescence microscopy (CORTEX) and a fundus photography (DRIVE) dataset. We show that manually annotating only 0.03% of the vessels in the CORTEX dataset allows pre-training a network to reach, on average, a Dice score of 0.87 ± 0.02, which is close to the baseline score of 0.92 obtained when all vessels of the training split of the dataset are annotated. For the DRIVE dataset, on average, a Dice score of 0.74 ± 0.02 is obtained by annotating only 0.29% of the vessels, which is also close to the baseline Dice score of 0.81 obtained when all vessels are annotated.

Conclusion: The proposed method can be used for disentangling the geometry and texture of blood vessels, which allows a significant improvement of network pre-training performance when compared to other pre-training methods commonly used in the literature.