输入分辨率对视网膜血管分割性能的影响：在五个数据集上的实证研究
Effect of Input Resolution on Retinal Vessel Segmentation Performance: An Empirical Study Across Five Datasets
AI总结：
研究探讨了输入分辨率对视网膜血管分割的影响，通过五个数据集验证了不同下采样比例对薄血管检测灵敏度的影响，提出宽度分层敏感性指标，发现高分辨率数据集在下采样到编码器有效范围时薄血管检测灵敏度提高，而低分辨率数据集在原生分辨率时灵敏度最高。
摘要：
大多数用于视网膜血管分割的深度学习流程会将视网膜图像调整到满足GPU内存限制并实现均匀批量处理的大小。然而，这种调整对薄血管检测的影响仍缺乏研究。当高分辨率图像下采样时，薄血管会被降为子像素结构，在数据进入网络之前就会造成不可逆的信息损失。标准体积指标如Dice分数无法捕捉这种损失，因为厚血管像素主导了评估。我们通过在五个视网膜数据集（DRIVE、STARE、CHASE_DB1、HRF和FIVES）上训练基线UNet，以多种下采样比例进行研究，这些数据集的原生宽度范围从565到3504像素不等，其他设置保持不变。我们引入了宽度分层敏感性度量，分别评估薄（半宽<3像素）、中等（3到7像素）和厚（>7像素）血管检测，使用源自欧几里得距离变换的原生分辨率宽度估计。结果显示，对于高分辨率数据集（HRF、FIVES），当图像下采样到编码器有效范围时，薄血管灵敏度单调增加，峰值出现在处理宽度256至876像素之间。对于低至中等分辨率数据集（DRIVE、STARE、CHASE_DB1），薄血管灵敏度在原生分辨率或附近最高，任何下采样都会导致灵敏度下降。在所有五个数据集中，激进的下采样将薄血管灵敏度降低了最高15.8个百分点（DRIVE），而Dice分数保持相对稳定，证实仅凭Dice分数不足以评估微血管分割。
英文摘要：
Most deep learning pipelines for retinal vessel segmentation resize fundus images to satisfy GPU memory constraints and enable uniform batch processing. However, the impact of this resizing on thin vessel detection remains underexplored. When high resolution images are downsampled, thin vessels are reduced to subpixel structures, causing irreversible information loss even before the data enters the network. Standard volumetric metrics such as the Dice score do not capture this loss because thick vessel pixels dominate the evaluation. We investigated this effect by training a baseline UNet at multiple downsampling ratios across five fundus datasets (DRIVE, STARE, CHASE_DB1, HRF, and FIVES) with native widths ranging from 565 to 3504 pixels, keeping all other settings fixed. We introduce a width-stratified sensitivity metric that evaluates thin (half-width <3 pixels), medium (3 to 7 pixels), and thick (>7 pixels) vessel detection separately, using native resolution width estimates derived from a Euclidean distance transform. Results show that for high-resolution datasets (HRF, FIVES), thin vessel sensitivity improves monotonically as images are downsampled toward the encoder's effective operating range, peaking at processed widths between 256 and 876 pixels. For low-to-mid resolution datasets (DRIVE, STARE, CHASE_DB1), thin vessel sensitivity is highest at or near native resolution and degrades with any downsampling. Across all five datasets, aggressive downsampling reduced thin vessel sensitivity by up to 15.8 percentage points (DRIVE) while Dice remained relatively stable, confirming that Dice alone is insufficient for evaluating microvascular segmentation.
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