Wang Lianyu, Wang Meng, Wang Tingting, Meng Qingquan, Zhou Yi, Peng Yuanyuan, Zhu Weifang, Chen Zhongyue, Chen Xinjian
School of Electronics and Information Engineering, Soochow University, Suzhou, China.
State Key Laboratory of Radiation Medicine and Protection, Soochow University, Suzhou, China.
Front Neurosci. 2021 Dec 24;15:797166. doi: 10.3389/fnins.2021.797166. eCollection 2021.
Choroid neovascularization (CNV) is one of the blinding factors. The early detection and quantitative measurement of CNV are crucial for the establishment of subsequent treatment. Recently, many deep learning-based methods have been proposed for CNV segmentation. However, CNV is difficult to be segmented due to the complex structure of the surrounding retina. In this paper, we propose a novel dynamic multi-hierarchical weighting segmentation network (DW-Net) for the simultaneous segmentation of retinal layers and CNV. Specifically, the proposed network is composed of a residual aggregation encoder path for the selection of informative feature, a multi-hierarchical weighting connection for the fusion of detailed information and abstract information, and a dynamic decoder path. Comprehensive experimental results show that our proposed DW-Net achieves better performance than other state-of-the-art methods.
脉络膜新生血管（CNV）是致盲因素之一。CNV的早期检测和定量测量对于后续治疗方案的制定至关重要。近年来，人们提出了许多基于深度学习的方法用于CNV分割。然而，由于周围视网膜结构复杂，CNV难以分割。在本文中，我们提出了一种新颖的动态多层次加权分割网络（DW-Net），用于同时分割视网膜层和CNV。具体而言，所提出的网络由用于选择信息性特征的残差聚合编码器路径、用于融合详细信息和抽象信息的多层次加权连接以及动态解码器路径组成。综合实验结果表明，我们提出的DW-Net比其他现有最先进方法具有更好的性能。
