SPNet: A novel deep neural network for retinal vessel segmentation based on shared decoder and pyramid-like loss
SPNet: A novel deep neural network for retinal vessel segmentation based on shared decoder and pyramid-like loss
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SPNet：一种基于共享解码器和金字塔式损失的新型视网膜血管分割深度神经网络
DOI：
10.1016/j.neucom.2022.12.039
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发表时间：
2022-02
期刊：
影响因子：
6
通讯作者：
Chuan-Xian Ren
中科院分区：
文献类型：
--
作者：
Geng-Xin Xu;Chuan-Xian Ren
视网膜血管图像的分割对视网膜病变的诊断至关重要。近年来，卷积神经网络在血管结构提取方面表现出了显著的能力。然而，由于视网膜血管的厚度不一致和边界模糊，对毛细血管和边缘的精细分割仍然是一个挑战。为了解决上述问题，本文提出了一种基于共享解码器和金字塔样损失（SPNet）的视网膜血管分割深度神经网络。具体来说，我们引入了一种解码共享机制来捕获多尺度语义信息，其中不同尺度的特征映射通过一系列权重共享解码模块进行解码。此外，为了加强对毛细血管和血管边缘的表征，我们定义了一个残差金字塔结构，该结构在解码阶段分解空间信息。设计了一个金字塔形的损失函数来逐步补偿可能出现的分割误差。公共基准测试的实验结果表明，该方法优于骨干网和大多数最先进的方法，特别是在毛细血管和血管轮廓区域。此外，在跨数据集上的性能验证了SPNet具有更强的泛化能力。
Segmentation of retinal vessel images is critical to the diagnosis of retinopathy. Recently, convolutional neural networks have shown significant ability to extract the blood vessel structure. However, it remains challenging to refined segmentation for the capillaries and the edges of retinal vessels due to thickness inconsistencies and blurry boundaries. In this paper, we propose a novel deep neural network for retinal vessel segmentation based on shared decoder and pyramid-like loss (SPNet) to address the above problems. Specifically, we introduce a decoder-sharing mechanism to capture multi-scale semantic information, where feature maps at diverse scales are decoded through a sequence of weight-sharing decoder modules. Also, to strengthen characterization on the capillaries and the edges of blood vessels, we define a residual pyramid architecture which decomposes the spatial information in the decoding phase. A pyramid-like loss function is designed to compensate possible segmentation errors progressively. Experimental results on public benchmarks show that the proposed method outperforms the backbone network and most state-of-the-art methods, especially in the regions of the capillaries and the vessel contours. In addition, performances on cross-datasets verify that SPNet shows stronger generalization ability.
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影响因子：
7.6
作者：
Budai A;Bock R;Maier A;Hornegger J;Michelson G
通讯作者：
Michelson G
DOI：
10.1007/978-3-319-67558-9_28
发表时间：
2017-09-09
期刊：
Deep learning in medical image analysis and multimodal learning for clinical decision support : Third International Workshop, DLMIA 2017, and 7th International Workshop, ML-CDS 2017, held in conjunction with MICCAI 2017 Quebec City, QC,..
影响因子：
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作者：
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通讯作者：
Jorge Cardoso M
DOI：
--
发表时间：
2019
期刊：
--
影响因子：
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作者：
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通讯作者：
Jian Sun-;Zongben Xu
影响因子：
19.5
作者：
Xie, Saining;Tu, Zhuowen
通讯作者：
Tu, Zhuowen
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影响因子：
6
作者：
Feng, Shouting;Zhuo, Zhongshuo;Tian, Qi
通讯作者：
Tian, Qi