Dense Dilated Network with Probability Regularized Walk for Vessel Detection

Deep Dilated Convolutional Nets for the Automatic Segmentation of Retinal Vessels

The reliable segmentation of retinal vasculature can provide the means to diagnose and monitor the progression of a variety of diseases affecting the blood vessel network, including diabetes and hypertension. We leverage the power of convolutional neural networks to devise a reliable and fully automated method that can accurately detect, segment, and analyze retinal vessels. In particular, we propose a novel, fully convolutional deep neural network with an encoder-decoder architecture that employs dilated spatial pyramid pooling with multiple dilation rates to recover the lost content in the e…

Retinal Vessels Segmentation Based on Dilated Multi-Scale Convolutional Neural Network

Accurate segmentation of retinal vessels is a basic step in Diabetic retinopathy(DR) detection. Most methods based on deep convolutional neural network (DCNN) have small receptive fields, and hence they are unable to capture global context information of larger regions, with difficult to identify lesions. The final segmented retina vessels contain more noise with low classification accuracy. Therefore, in this paper, we propose a DCNN structure named as D-Net. In the proposed D-Net, the dilation convolution is used in the backbone network to obtain a larger receptive field without losing spati…

Contextual Information Enhanced Convolutional Neural Networks for Retinal Vessel Segmentation in Color Fundus Images

Accurate retinal vessel segmentation is a challenging problem in color fundus image analysis. An automatic retinal vessel segmentation system can effectively facilitate clinical diagnosis and ophthalmological research. Technically, this problem suffers from various degrees of vessel thickness, perception of details, and contextual feature fusion. For addressing these challenges, a deep learning based method has been proposed and several customized modules have been integrated into the well-known encoder-decoder architecture U-net, which is mainly employed in medical image segmentation. Structu…

(M)SLAe-Net: Multi-Scale Multi-Level Attention embedded Network for Retinal Vessel Segmentation

Segmentation plays a crucial role in diagnosis. Studying the retinal vasculatures from fundus images help identify early signs of many crucial illnesses such as diabetic retinopathy. Due to the varying shape, size, and patterns of retinal vessels, along with artefacts and noises in fundus images, no one-stage method can accurately segment retinal vessels. In this work, we propose a multi-scale, multi-level attention embedded CNN architecture ((M)SLAe-Net) to address the issue of multi-stage processing for robust and precise segmentation of retinal vessels. We do this by extracting features at …

LUNet：用于高分辨率眼底图像中细动脉和细静脉分割的深度学习方法

LUNet: Deep Learning for the Segmentation of Arterioles and Venules in High Resolution Fundus Images

针对视网膜细动脉和细静脉的自动化分割问题，提出新型深度学习架构LUNet，通过双重膨胀卷积块和高分辨率长尾结构优化分割性能，并在多个测试集上显著优于现有算法。

以冠状动脉CT血管造影中牧羊杖形RCA自动检测为例处理标签不确定性

Handling Label Uncertainty on the Example of Automatic Detection of Shepherd's Crook RCA in Coronary CT Angiography

针对牧羊杖形右冠状动脉标注存在定性不确定性的问题，本文提出基于残差扩张卷积的一维CNN，并比较随机分配、排除、软标签及拒识策略，最佳AUC达0.938。