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首页> 外文会议>Annual International Conference of the IEEE Engineering in Medicine and Biology Society >Deep neural ensemble for retinal vessel segmentation in fundus images towards achieving label-free angiography
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Deep neural ensemble for retinal vessel segmentation in fundus images towards achieving label-free angiography

机译：深度神经集合体用于眼底图像中的视网膜血管分割，以实现无标记血管造影

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摘要

Automated segmentation of retinal blood vessels in label-free fundus images entails a pivotal role in computed aided diagnosis of ophthalmic pathologies, viz., diabetic retinopathy, hypertensive disorders and cardiovascular diseases. The challenge remains active in medical image analysis research due to varied distribution of blood vessels, which manifest variations in their dimensions of physical appearance against a noisy background. In this paper we formulate the segmentation challenge as a classification task. Specifically, we employ unsupervised hierarchical feature learning using ensemble of two level of sparsely trained denoised stacked autoencoder. First level training with bootstrap samples ensures decoupling and second level ensemble formed by different network architectures ensures architectural revision. We show that ensemble training of auto-encoders fosters diversity in learning dictionary of visual kernels for vessel segmentation. SoftMax classifier is used for fine tuning each member autoencoder and multiple strategies are explored for 2-level fusion of ensemble members. On DRIVE dataset, we achieve maximum average accuracy of 95.33% with an impressively low standard deviation of 0.003 and Kappa agreement coefficient of 0.708. Comparison with other major algorithms substantiates the high efficacy of our model.
机译：无标签眼底图像中视网膜血管的自动分割在眼科病理学，糖尿病性视网膜病，高血压疾病和心血管疾病的计算机辅助诊断中起着关键作用。由于血管的分布各不相同，因此在医学图像分析研究中仍然面临挑战，这表明在嘈杂的背景下其物理外观尺寸存在变化。在本文中，我们将分割挑战表述为分类任务。具体来说，我们采用两层稀疏训练的去噪堆叠式自动编码器的集成进行无监督的层次特征学习。使用引导程序样本进行的第一级培训可确保解耦，而由不同网络体系结构形成的第二级集成可确保体系结构修订。我们表明，自动编码器的合奏训练在学习视觉核字典中进行血管分割方面促进了多样性。 SoftMax分类器用于微调每个成员的自动编码器，并探索了用于集成成员的2级融合的多种策略。在DRIVE数据集上，我们实现了95.33％的最大平均准确度，以及0.003的极低标准偏差和0.708的Kappa协议系数。与其他主要算法的比较证实了我们模型的高效率。

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