With the recent advent of deep learning in medical image processing, retinal blood vessel segmentation topic has been comprehensively handled by numerous research works. However, since the ratio between the number of vessel and background pixels is heavily imbalanced, many attempts utilized patches augmented from original fundus images along with fully convolutional networks for addressing such pixel-wise labeling problem, which significantly costs computational resources. In this paper, a method using Round-wise Features Aggregation on Bracket-shaped convolutional neural networks (RFA-BNet) is proposed to exclude the necessity of patches augmentation while efficiently handling the irregular and diverse representation of retinal vessels. Particularly, given raw fundus images, typical feature maps extracted from a pretrained backbone network are employed for a bracket-shaped decoder, wherein middle-scale features are continuously exploited round-by-round. Then, the decoded maps having highest resolution of each round are aggregated to enable the built model to flexibly learn various degrees of embedded semantic details while retaining proper annotations of thin and small vessels. Finally, the proposed approach showed its effectiveness in terms of sensitivity (0.7932), specificity (0.9741), accuracy (0.9511), and AUROC (0.9732) on DRIVE dataset.