发表机构
Federal University of São Carlos（圣卡洛斯联邦大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究卷积神经网络在荧光显微镜和视网膜眼底摄影领域分割血管的视觉线索，通过系列实验量化形状、纹理和感受野对分割性能的影响，发现像素强度更重要，该方法为审核和改进血管成像深度学习系统提供定量基础。
AI 中文摘要
血管分割是临床诊断的标准程序，但决定模型决策的具体视觉特征仍知之甚少。本文研究卷积神经网络（CNN）在荧光显微镜和视网膜眼底摄影这两个不同成像领域中用于分割血管的视觉线索。通过一系列实验量化形状、纹理和感受野对分割性能的影响。先通过对像素洗牌和归一化补丁评估来分离纹理和强度；再通过在稀疏轮廓和中心线上训练模型评估全局形状相关性；最后通过系统改变网络理论和有效感受野量化所需空间上下文。发现在评估数据集范围内，像素强度比纹理更相关，即使去除两者线索网络仍保持高精度。此外，CNN仅从形状线索推断完整血管几何结构有困难，通常依赖约20像素的较小有效感受野，不过全局上下文对眼底图像有一定帮助。此方法为审核和改进血管成像中的深度学习系统提供了定量基础。
英文摘要
Vascular segmentation is a standard procedure for clinical diagnosis, yet the specific visual features determining model decisions remain poorly understood. This paper investigates the visual cues Convolutional Neural Networks (CNNs) use to segment blood vessels across two distinct imaging domains: fluorescence microscopy and retinal fundus photography. We employ a series of experiments to quantify the influence of shape, texture, and receptive field on segmentation performance. First, we isolate texture and intensity by evaluating performance on patches subjected to pixel shuffling and normalization. Second, we assess global shape relevance by training models on sparse contours and centerlines. Lastly, we quantify the required spatial context by systematically varying the network's theoretical and effective receptive fields. Within the scope of the evaluated datasets, we found that pixel intensity is more relevant than texture, though networks maintain surprisingly high accuracy even when both cues are removed. Furthermore, CNNs struggle to extrapolate full vessel geometry from shape cues alone, typically relying on a relatively small effective receptive field of around 20 pixels, though global context provides a modest benefit for fundus images. While specific to the modalities studied, this methodology offers a quantitative foundation to audit and refine deep learning systems in vascular imaging.
