Xi Xiaoming, Meng Xianjing, Qin Zheyun, Nie Xiushan, Yin Yilong, Chen Xinjian
School of Computer Science and Technology, Shandong Jianzhu University, 250101, China.
School of Computer Science and Technology, Shandong University of Finance and Economics, 250014, China.
Biomed Opt Express. 2020 Oct 7;11(11):6122-6136. doi: 10.1364/BOE.400816. eCollection 2020 Nov 1.
Choroidal neovascularization (CNV) is a characteristic feature of wet age-related macular degeneration (AMD). Quantification of CNV is useful to clinicians in the diagnosis and treatment of CNV disease. Before quantification, CNV lesion should be delineated by automatic CNV segmentation technology. Recently, deep learning methods have achieved significant success for medical image segmentation. However, some CNVs are small objects which are hard to discriminate, resulting in performance degradation. In addition, it's difficult to train an effective network for accurate segmentation due to the complicated characteristics of CNV in OCT images. In order to tackle these two challenges, this paper proposed a novel Informative Attention Convolutional Neural Network (IA-net) for automatic CNV segmentation in OCT images. Considering that the attention mechanism has the ability to enhance the discriminative power of the interesting regions in the feature maps, the attention enhancement block is developed by introducing the additional attention constraint. It has the ability to force the model to pay high attention on CNV in the learned feature maps, improving the discriminative ability of the learned CNV features, which is useful to improve the segmentation performance on small CNV. For accurate pixel classification, the novel informative loss is proposed with the incorporation of an informative attention map. It can focus training on a set of informative samples that are difficult to be predicted. Therefore, the trained model has the ability to learn enough information to classify these informative samples, further improving the performance. The experimental results on our database demonstrate that the proposed method outperforms traditional CNV segmentation methods.
脉络膜新生血管（CNV）是湿性年龄相关性黄斑变性（AMD）的一个特征性表现。CNV的定量分析对临床医生诊断和治疗CNV疾病很有用。在进行定量分析之前，应通过自动CNV分割技术勾勒出CNV病变。近年来，深度学习方法在医学图像分割方面取得了显著成功。然而，一些CNV是难以区分的小目标，导致性能下降。此外，由于OCT图像中CNV的复杂特征，很难训练出一个有效的网络进行精确分割。为了应对这两个挑战，本文提出了一种新颖的信息注意力卷积神经网络（IA-net）用于OCT图像中的自动CNV分割。考虑到注意力机制有能力增强特征图中感兴趣区域的辨别力，通过引入额外的注意力约束开发了注意力增强模块。它能够迫使模型在学习到的特征图中高度关注CNV，提高学习到的CNV特征的辨别能力，这有助于提高对小CNV的分割性能。为了进行精确的像素分类，结合信息注意力图提出了新颖的信息损失函数。它可以将训练集中在一组难以预测的信息性样本上。因此，训练后的模型有能力学习足够的信息来对这些信息性样本进行分类，进一步提高性能。在我们的数据库上的实验结果表明，所提出的方法优于传统的CNV分割方法。
