Drusen are an early sign of non-neovascular age-related macular degeneration which is a major factor of irreversible blindness. Drusen segmentation plays a vital role in proper diagnosis and prevention of further complications. However, most of the existing drusen segmentation approaches rely on handcrafted features which are not always guaranteed to be discriminative and therefore lead to limited performance. In this paper, we propose a deep feature extraction framework for drusen segmentation. It is formulated as a deep model which can automatically extract discriminative features. Specifically, the framework is mainly composed of three components, including feature learning, loss function and classification. The effectiveness of our method lies in the fact that the deep feature learning procedures are driven by an adaptive collaborative similarity learning technique in loss function. We evaluate the framework on STARE and DRIVE datasets, and the quantitative comparison with the state-of-the-art methods in terms of sensitivity, specificity and accuracy demonstrates the superiority of the proposed method.

中文翻译：

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通过自适应协作学习进行深度特征提取，用于眼底图像的玻璃疣分割

玻璃疣是非新生血管性年龄相关性黄斑变性的早期征兆，是不可逆失明的主要因素。玻璃疣分割在正确诊断和预防进一步并发症方面起着至关重要的作用。然而，大多数现有的玻璃疣分割方法依赖于手工制作的特征，这些特征并不总是保证具有区分性，因此导致性能有限。在本文中，我们提出了一种用于玻璃疣分割的深度特征提取框架。它被制定为一个深度模型，可以自动提取判别特征。具体来说，该框架主要由特征学习、损失函数和分类三个部分组成。我们方法的有效性在于深度特征学习过程是由损失函数中的自适应协作相似性学习技术驱动的。我们在 STARE 和 DRIVE 数据集上评估框架，与最先进方法在灵敏度、特异性和准确性方面的定量比较证明了所提出方法的优越性。