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.

翻译：血管分割在众多医学图像应用中至关重要，例如检测冠状动脉狭窄、视网膜血管疾病和脑动脉瘤。然而，实现高像素级精度、完整的拓扑结构以及对各种对比度变化的鲁棒性是一项关键且具有挑战性的任务，现有方法大多仅侧重实现其中一两个方面。本文提出了一种新颖方法——亲和特征强化网络（AFN），该方法通过对比度不敏感的多尺度亲和力方法联合建模几何特征并优化像素级分割特征。具体而言，我们为每个像素计算多尺度亲和力场，捕获其与预测掩膜图像中相邻像素的语义关系。该场表征不同尺寸血管段的局部几何结构，使我们能够学习空间和尺度感知的自适应权重以强化血管特征。我们在四种不同类型的血管数据集上评估了AFN：X射线血管造影冠状动脉数据集（XCAD）、门静脉数据集（PV）、数字减影脑血管造影数据集（DSA）和视网膜血管数据集（DRIVE）。大量实验结果表明，AFN在更高精度和拓扑指标方面均优于现有最先进方法，同时对各种对比度变化具有更强的鲁棒性。本工作的源代码可从https://github.com/TY-Shi/AFN获取。