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. 2021 Dec 24:15:797166.
doi: 10.3389/fnins.2021.797166. eCollection 2021.

DW-Net: Dynamic Multi-Hierarchical Weighting Segmentation Network for Joint Segmentation of Retina Layers With Choroid Neovascularization

Lianyu Wang  1 , Meng Wang  1 , Tingting Wang  1 , Qingquan Meng  1 , Yi Zhou  1 , Yuanyuan Peng  1 , Weifang Zhu  1 , Zhongyue Chen  1 , Xinjian Chen  1   2
Affiliations

DW-Net: Dynamic Multi-Hierarchical Weighting Segmentation Network for Joint Segmentation of Retina Layers With Choroid Neovascularization

Lianyu Wang et al. Front Neurosci. .

Abstract

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.

Keywords: attention mechanism; choroid neovascularization; convolutional neural network; medical image processing; multi-target segmentation; optical coherence tomography.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Optical coherence tomography (OCT) image of the normal retinal layer. (A) Original image. (B) Label. NFL, nerve fiber layer; GCL, ganglion cell layer; IPL, inner plexiform layer; INL, inner nuclear layer; OPL, outer plexiform layer; ONL, outer nuclear layer; OPSL, outer photoreceptor segment layer; RPE, retinal pigment epithelium.
FIGURE 2
FIGURE 2
Optical coherence tomography (OCT) image of the normal retinal layer containing choroid neovascularization (CNV). (A) Original image. (B) Label.
FIGURE 3
FIGURE 3
(A) Architecture of the proposed dynamic multi-hierarchical weighting segmentation network (DW-Net). The dark yellow part in (B,C) indicate the residual aggregation encoder path and the dynamic multi-hierarchical weighting connection, respectively.
FIGURE 4
FIGURE 4
Architecture of the UNet++ by Zhou et al. (2018).
FIGURE 5
FIGURE 5
Visualization results of the different methods.
FIGURE 6
FIGURE 6
Histogram of choroid neovascularization (CNV) volume comparison.
FIGURE 7
FIGURE 7
Architecture of Res18UNet++ (A) and AdaptiveUNet++ (B).
FIGURE 8
FIGURE 8
Value of the learnable parameter αi,2j+k during training.
See this image and copyright information in PMC

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