Chudzik Piotr, Al-Diri Bashir, Caliva Francesco, Hunter Andrew
Annu Int Conf IEEE Eng Med Biol Soc. 2018 Jul;2018:5934-5937. doi: 10.1109/EMBC.2018.8513604.
This paper presents a novel two-stage vessel segmentation framework applied to retinal fundus images. In the first stage a convolutional neural network (CNN) is used to correlate an image patch with a corresponding groundtruth reduced using Totally Random Trees Embedding. In the second stage training patches are forward propagated through CNN to create a visual codebook. The codebook is used to build a generative nearest neighbour search space that can be queried by feature vectors created through forward propagating previously-unseen patches through CNN. The proposed framework is able to generate segmentation patches that were not seen during training. Evaluated using publicly available datasets (DRIVE, STARE) demonstrated better performance than state-of-the-art methods in terms of multiple evaluation metrics. The accuracy, robustness, speed and simplicity of the proposed framework demonstrates its suitability for automated vessel segmentation.
本文提出了一种应用于视网膜眼底图像的新型两阶段血管分割框架。在第一阶段，使用卷积神经网络（CNN）将图像块与使用完全随机树嵌入法简化后的相应真值进行关联。在第二阶段，训练块通过CNN进行前向传播以创建视觉码本。该码本用于构建一个生成式最近邻搜索空间，该空间可由通过将先前未见的块通过CNN进行前向传播而创建的特征向量进行查询。所提出的框架能够生成训练期间未见的分割块。使用公开可用数据集（DRIVE、STARE）进行评估表明，在多个评估指标方面，该框架比现有方法具有更好的性能。所提出框架的准确性、鲁棒性、速度和简单性证明了其适用于自动血管分割。
