School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China; Hefei Innovation Research Institute, Beihang University, Hefei, 230012, Anhui, China.
School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China; Hefei Innovation Research Institute, Beihang University, Hefei, 230012, Anhui, China; Beijing Advanced Innovation Centre for Big Data-Based Precision Medicine, Beihang University, Beijing, 100083, China.
Comput Biol Med. 2024 Jan;168:107633. doi: 10.1016/j.compbiomed.2023.107633. Epub 2023 Nov 4.
Recent deep learning methods with convolutional neural networks (CNNs) have boosted advance prosperity of medical image analysis and expedited the automatic retinal artery/vein (A/V) classification. However, it is challenging for these CNN-based approaches in two aspects: (1) specific tubular structures and subtle variations in appearance, contrast, and geometry, which tend to be ignored in CNNs with network layer increasing; (2) limited well-labeled data for supervised segmentation of retinal vessels, which may hinder the effectiveness of deep learning methods. To address these issues, we propose a novel semi-supervised point consistency network (SPC-Net) for retinal A/V classification. SPC-Net consists of an A/V classification (AVC) module and a multi-class point consistency (MPC) module. The AVC module adopts an encoder-decoder segmentation network to generate the prediction probability map of A/V for supervised learning. The MPC module introduces point set representations to adaptively generate point set classification maps of the arteriovenous skeleton, which enjoys its prediction flexibility and consistency (i.e. point consistency) to effectively alleviate arteriovenous confusion. In addition, we propose a consistency regularization between the predicted A/V classification probability maps and point set representations maps for unlabeled data to explore the inherent segmentation perturbation of the point consistency, reducing the need for annotated data. We validate our method on two typical public datasets (DRIVE, HRF) and a private dataset (TR280) with different resolutions. Extensive qualitative and quantitative experimental results demonstrate the effectiveness of our proposed method for supervised and semi-supervised learning.
最近，基于卷积神经网络（CNN）的深度学习方法极大地推动了医学图像分析的发展，并加速了视网膜动脉/静脉（A/V）的自动分类。然而，这些基于 CNN 的方法在两个方面具有挑战性：（1）特定的管状结构以及外观、对比度和几何形状的细微变化，这在网络层增加的 CNN 中往往会被忽略；（2）用于视网膜血管监督分割的有限良好标记数据，这可能会阻碍深度学习方法的有效性。为了解决这些问题，我们提出了一种新颖的半监督点一致性网络（SPC-Net）用于视网膜 A/V 分类。SPC-Net 由 A/V 分类（AVC）模块和多类点一致性（MPC）模块组成。AVC 模块采用编码器-解码器分割网络，为监督学习生成 A/V 的预测概率图。MPC 模块引入点集表示，自适应地生成动静脉骨架的点集分类图，具有预测灵活性和一致性（即点一致性），可有效缓解动静脉混淆。此外，我们提出了一种预测 A/V 分类概率图和未标记数据的点集表示图之间的一致性正则化方法，以探索点一致性的内在分割扰动，减少对注释数据的需求。我们在两个具有不同分辨率的典型公共数据集（DRIVE、HRF）和一个私有数据集（TR280）上验证了我们的方法。广泛的定性和定量实验结果表明，我们的方法在监督和半监督学习中都具有有效性。
