Dense Dilated Network With Probability Regularized Walk for Vessel Detection
Dense Dilated Network With Probability Regularized Walk for Vessel Detection
复制标题
用于船舶检测的具有概率正则游走的密集扩张网络
DOI：
10.1109/tmi.2019.2950051
复制
发表时间：
2020-05-01
影响因子：
10.6
通讯作者：
Liu, Jiang
中科院分区：
文献类型：
--
作者：
Mou, Lei;Chen, Li;Liu, Jiang
视网膜血管的检测在许多眼科疾病的诊断和治疗中具有重要意义。目前已经提出了多种血管检测方法。然而，大多数算法忽略了血管的连通性，这在诊断中起着重要的作用。本文提出了一种新的视网膜血管检测方法。该方法包括一个密集的扩张网络来获得血管的初始检测，以及一种概率正则化行走算法来解决初始检测中的断裂问题。密集扩张网络将新提出的密集扩张特征提取块集成到编解码器结构中，以提取和累积不同尺度上的特征。采用多尺度骰子损失函数对网络进行训练。为了提高分割血管的连通性，我们还引入了一种概率正则行走算法来连接断裂的血管。该方法已经在三个公共数据集上得到应用：DRIVE、STARE和CHASE_DB1。结果表明，该方法在准确度、灵敏度、特异度和受试者工作特性曲线下面积等方面均优于最新方法。
The detection of retinal vessel is of great importance in the diagnosis and treatment of many ocular diseases. Many methods have been proposed for vessel detection. However, most of the algorithms neglect the connectivity of the vessels, which plays an important role in the diagnosis. In this paper, we propose a novel method for retinal vessel detection. The proposed method includes a dense dilated network to get an initial detection of the vessels and a probability regularized walk algorithm to address the fracture issue in the initial detection. The dense dilated network integrates newly proposed dense dilated feature extraction blocks into an encoder-decoder structure to extract and accumulate features at different scales. A multi-scale Dice loss function is adopted to train the network. To improve the connectivity of the segmented vessels, we also introduce a probability regularized walk algorithm to connect the broken vessels. The proposed method has been applied on three public data sets: DRIVE, STARE and CHASE_DB1. The results show that the proposed method outperforms the state-of-the-art methods in accuracy, sensitivity, specificity and also area under receiver operating characteristic curve.