College of Computer Science, Sichuan University, Chengdu, 610065, China.
School of Science, Inner Mongolia University of Science and Technology, Baotou, 014010, China.
Int J Comput Assist Radiol Surg. 2017 Dec;12(12):2181-2193. doi: 10.1007/s11548-017-1619-0. Epub 2017 Jun 2.
Changes in the appearance of retinal blood vessels are an important indicator for various ophthalmologic and cardiovascular diseases, including diabetes, hypertension, arteriosclerosis, and choroidal neovascularization. Vessel segmentation from retinal images is very challenging because of low blood vessel contrast, intricate vessel topology, and the presence of pathologies such as microaneurysms and hemorrhages. To overcome these challenges, we propose a neural network-based method for vessel segmentation.
A deep supervised fully convolutional network is developed by leveraging multi-level hierarchical features of the deep networks. To improve the discriminative capability of features in lower layers of the deep network and guide the gradient back propagation to overcome gradient vanishing, deep supervision with auxiliary classifiers is incorporated in some intermediate layers of the network. Moreover, the transferred knowledge learned from other domains is used to alleviate the issue of insufficient medical training data. The proposed approach does not rely on hand-crafted features and needs no problem-specific preprocessing or postprocessing, which reduces the impact of subjective factors.
We evaluate the proposed method on three publicly available databases, the DRIVE, STARE, and CHASE_DB1 databases. Extensive experiments demonstrate that our approach achieves better or comparable performance to state-of-the-art methods with a much faster processing speed, making it suitable for real-world clinical applications. The results of cross-training experiments demonstrate its robustness with respect to the training set.
The proposed approach segments retinal vessels accurately with a much faster processing speed and can be easily applied to other biomedical segmentation tasks.
视网膜血管外观的变化是各种眼科和心血管疾病的重要指标，包括糖尿病、高血压、动脉硬化和脉络膜新生血管形成。由于血管对比度低、血管拓扑结构复杂以及微动脉瘤和出血等病变的存在，从视网膜图像中进行血管分割极具挑战性。为了克服这些挑战，我们提出了一种基于神经网络的血管分割方法。
通过利用深度网络的多级分层特征，开发了一种深度监督的全卷积网络。为了提高深度网络低层特征的判别能力，并引导梯度反向传播以克服梯度消失，在网络的一些中间层中引入了辅助分类器的深度监督。此外，还利用从其他领域转移的知识来缓解医学训练数据不足的问题。所提出的方法不依赖于手工制作的特征，也不需要特定于问题的预处理或后处理，从而减少了主观因素的影响。
我们在三个公开可用的数据库，即 DRIVE、STARE 和 CHASE_DB1 数据库上评估了所提出的方法。大量实验表明，与最先进的方法相比，我们的方法具有更好或相当的性能，并且处理速度更快，非常适合实际的临床应用。交叉训练实验的结果表明了它对训练集的稳健性。
所提出的方法以更快的处理速度准确地分割视网膜血管，并且可以轻松应用于其他生物医学分割任务。
