  Vessel segmentation is crucial in many medical image applications, such as
detecting coronary stenoses, retinal vessel diseases and brain aneurysms.
However, achieving high pixel-wise accuracy, complete topology structure and
robustness to various contrast variations are critical and challenging, and
most existing methods focus only on achieving one or two of these aspects. In
this paper, we present a novel approach, the affinity feature strengthening
network (AFN), which jointly models geometry and refines pixel-wise
segmentation features using a contrast-insensitive, multiscale affinity
approach. Specifically, we compute a multiscale affinity field for each pixel,
capturing its semantic relationships with neighboring pixels in the predicted
mask image. This field represents the local geometry of vessel segments of
different sizes, allowing us to learn spatial- and scale-aware adaptive weights
to strengthen vessel features. We evaluate our AFN on four different types of
vascular datasets: X-ray angiography coronary vessel dataset (XCAD), portal
vein dataset (PV), digital subtraction angiography cerebrovascular vessel
dataset (DSA) and retinal vessel dataset (DRIVE). Extensive experimental
results demonstrate that our AFN outperforms the state-of-the-art methods in
terms of both higher accuracy and topological metrics, while also being more
robust to various contrast changes. The source code of this work is available
at https://github.com/TY-Shi/AFN.
