Abstract：
Retinal vessel segmentation is crucial for the diagnosis of ophthalmic and cardiovascular diseases. However, retinal vessels are densely and irregularly distributed, with many capillaries blending into the background, and exhibit low contrast. Moreover, the encoder-decoder-based network for retinal vessel segmentation suffers from irreversible loss of detailed features due to multiple encoding and decoding, leading to incorrect segmentation of the vessels. Meanwhile, the single-dimensional attention mechanisms possess limitations, neglecting the importance of multidimensional features. To solve these issues, in this paper, we propose a detail-enhanced attention feature fusion network (DEAF-Net) for retinal vessel segmentation. First, the detail-enhanced residual block (DERB) module is proposed to strengthen the capacity for detailed representation, ensuring that intricate features are efficiently maintained during the segmentation of delicate vessels. Second, the multidimensional collaborative attention encoder (MCAE) module is proposed to optimize the extraction of multidimensional information. Then, the dynamic decoder (DYD) module is introduced to preserve spatial information during the decoding process and reduce the information loss caused by upsampling operations. Finally, the proposed detail-enhanced feature fusion (DEFF) module composed of DERB, MCAE and DYD modules fuses feature maps from both encoding and decoding and achieves effective aggregation of multi-scale contextual information. The experiments conducted on the datasets of DRIVE, CHASEDB1, and STARE, achieving Sen of 0.8305, 0.8784, and 0.8654, and AUC of 0.9886, 0.9913, and 0.9911 on DRIVE, CHASEDB1, and STARE, respectively, demonstrate the performance of our proposed network, particularly in the segmentation of fine retinal vessels.
摘要：
视网膜血管分割对于眼科和心血管疾病的诊断至关重要。然而,视网膜血管密集且不规则分布，许多毛细血管融合在背景中，并表现出低对比度。此外，基于编码器-解码器的视网膜血管分割网络由于多次编码和解码而遭受详细特征的不可逆转的损失，导致血管的不正确分割。同时,单维注意力机制具有局限性，忽视了多维特征的重要性。为了解决这些问题,在本文中，我们提出了一种用于视网膜血管分割的细节增强注意力特征融合网络(DEAF-Net)。首先,提出了细节增强残差块(DERB)模块，以增强详细表示的能力，确保在精细血管的分割过程中有效地保持复杂的特征。第二,提出了多维协同注意编码器(MCAE)模块来优化多维信息的提取。然后，引入动态解码器(DYD)模块,在解码过程中保留空间信息,减少上采样操作造成的信息损失。最后,所提出的由DERB组成的细节增强特征融合(DEFF)模块，MCAE和DYD模块融合了编码和解码的特征图，实现了多尺度上下文信息的有效聚合。在DRIVE的数据集上进行的实验，CHASEDB1和STARE,在DRIVE上实现了0.8305、0.8784和0.8654的Sen，以及0.9886、0.9913和0.9911的AUC，CHASEDB1和STARE,分别,展示我们提出的网络的性能，特别是在细视网膜血管的分割中。
