  Many studies regarding the vasculature of biological tissues involve the
segmentation of the blood vessels in a sample followed by the creation of a
graph structure to model the vasculature. The graph is then used to extract
relevant vascular properties. Small segmentation errors can lead to largely
distinct connectivity patterns and a high degree of variability of the
extracted properties. Nevertheless, global metrics such as Dice, precision, and
recall are commonly applied for measuring the performance of blood vessel
segmentation algorithms. These metrics might conceal important information
about the accuracy at specific regions of a sample. To tackle this issue, we
propose a local vessel salience (LVS) index to quantify the expected difficulty
in segmenting specific blood vessel segments. The LVS index is calculated for
each vessel pixel by comparing the local intensity of the vessel with the image
background around the pixel. The index is then used for defining a new accuracy
metric called low-salience recall (LSRecall), which quantifies the performance
of segmentation algorithms on blood vessel segments having low salience. The
perspective provided by the LVS index is used to define a data augmentation
procedure that can be used to improve the segmentation performance of
convolutional neural networks. We show that segmentation algorithms having high
Dice and recall values can display very low LSRecall values, which reveals
systematic errors of these algorithms for vessels having low salience. The
proposed data augmentation procedure is able to improve the LSRecall of some
samples by as much as 25%. The developed methodology opens up new possibilities
for comparing the performance of segmentation algorithms regarding
hard-to-detect blood vessels as well as their capabilities for vascular
topology preservation.
