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This work presents a controlled empirical study of loss functions for vessel segmentation using a U-Net architecture that employs strided convolutions in the encoder, together with a consistent pre-processing pipeline based on morphological enhancement and principal component analysis. We compare cross-entropy, weighted cross-entropy, and Dice losses on the DRIVE and STARE datasets under identical settings, reporting pixel-wise and overlap-based measures to reflect both detection and spatial agreement. The configuration with weighted cross-entropy provides a balanced outcome, achieving sensitivity and accuracy of 0.873 and 0.969 on DRIVE, and 0.821 and 0.961 on STARE. 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