Bridge-Net: Context-involved U-net with patch-based loss weight mapping for retinal blood vessel segmentation
Bridge-Net: Context-involved U-net with patch-based loss weight mapping for retinal blood vessel segmentation
复制标题
Bridge-Net：上下文相关的 U 网，具有基于补丁的损失权重映射，用于视网膜血管分割
DOI：
10.1016/j.eswa.2022.116526
复制
发表时间：
2022-02-03
影响因子：
8.5
通讯作者：
Gao, Xieping
中科院分区：
文献类型：
--
作者：
Zhang, Yuan;He, Miao;Gao, Xieping
眼底图像中的视网膜血管分割在视网膜疾病的早期诊断和治疗中起着重要的作用。近年来，基于深度神经网络的分割方法引起了专家学者的关注。然而，由于眼底图像中血管分布的复杂性以及血管与背景之间的不平衡性，视网膜血管分割仍然具有挑战性。本文提出了一种基于深度神经网络的视网膜血管分割方法。首先，我们提出了一种名为Bridge-net的新型深度网络架构，以有效地利用视网膜血管的上下文。具体来说，该架构将递归神经网络（RNN）集成到卷积神经网络（CNN）中，以提供上下文，然后生成视网膜血管的概率图。其次，我们提出了一个基于块的损失重量映射，考虑到不同类型的血管的分布，以纠正不平衡，因为厚和薄的血管之间有很大的形态差异。最后，我们在三个公开数据集STARE，DRIVE和CHASE_DB1上评估了我们的方法，并将结果与18种最先进的方法进行了比较。我们还将我们的方法与高分辨率数据集上的一些现有方法进行了比较，即，人权基金会。结果表明，我们的方法实现了更好的/可比的性能相比，现有的方法。在不同数据集上的实验结果也验证了该方法的有效性和稳定性。
Retinal blood vessel segmentation in fundus images plays an important role in the early diagnosis and treatment of retinal diseases. In recent years, the segmentation methods based on deep neural networks have attracted the attention of experts and scholars. However, due to the complexity of the distribution of blood vessels in fundus images and the imbalance between blood vessels and background, retinal blood vessel segmentation remains challenging. In this paper, we present a retinal blood vessel segmentation method using deep neural networks. Firstly, we propose a novel deep network architecture named Bridge-net to make use of the context of the retinal blood vessels efficiently. Specifically, the architecture incorporates a recurrent neural network (RNN) into a convolutional neural network (CNN) to deliver the context and then to produce the probability map of the retinal blood vessels. Secondly, we propose a patch-based loss weight mapping by considering the distributions of different types of blood vessels to correct the imbalance, since there are large morphological differences between thick and thin blood vessels. Finally, we evaluate our method on three publicly datasets STARE, DRIVE, and CHASE_DB1, and compare the results to eighteen state-of-the-art approaches. We also compare our method with some existing approaches on a high-resolution dataset, i.e., HRF. The results show that our method achieves better/comparable performances when compared to the existing approaches. The results on various datasets also verify the effectiveness and stability of the proposed method.