Optic cup and disc segmentation based on multi-level wavelet subband-assisted learning and hybrid convolution-based dual self-attention

IF 3 3区 工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC Digital Signal Processing Pub Date : 2025-09-01 Epub Date: 2025-04-22 DOI:10.1016/j.dsp.2025.105253
Fuying Wang , Suyu Wang , Shangjie Jin
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Abstract

Glaucoma is one of the three leading causes of blindness globally, with over 21 million patients affected in China alone. In clinical practice, accurate segmentation of the optic cup and optic disc is crucial for ophthalmologists in diagnosing glaucoma. Although significant progress has been made, especially with convolutional neural network-based methods, numerous challenges remain in scenarios such as blurred boundaries and blood vessels overlapping the boundaries. To address these issues, a novel optic cup and optic disc segmentation method based on multi-level wavelet subband-assisted learning and hybrid convolution-based dual self-attention are proposed. Firstly, a backbone network enhanced by multi-level wavelet subband auxiliary learning was designed. By introducing wavelet subband information of different directions and frequency bands at various levels of the backbone network, the network can focus more on key features relevant to the segmentation task and achieve more comprehensive feature extraction. Then, a hybrid convolution-based dual self-attention module is designed, which incorporates a ConvMixer module to extract diverse features, thereby enhancing the network's ability to adapt to different scales and shapes of the optic cups and discs. Subsequently, the features are processed by the dual self-attention module in both spatial and channel dimensions and are then reused for deeper feature extraction using different forms of convolution. Finally, feature map multiplication and skip connections are employed to suppress irrelevant features, amplify valuable ones, and refine the overall segmentation results of the optic cup and optic disc. Experimental results on the Drishti-Gs and RIM-ONE-r3 datasets show that the proposed method outperforms most current mainstream algorithms, and it achieves better segmentation results, especially along the edges of the optic cup and disc regions.

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基于多级小波辅助子带学习和混合卷积的双自注意光学杯盘分割
青光眼是全球三大致盲原因之一,仅在中国就有2100多万患者。在临床实践中,视杯和视盘的准确分割是眼科医生诊断青光眼的关键。尽管已经取得了重大进展,特别是基于卷积神经网络的方法,但在边界模糊和血管重叠等情况下仍然存在许多挑战。针对这些问题,提出了一种基于多级小波辅助子带学习和混合卷积双重自注意的视杯和视盘分割方法。首先,设计了多级小波子带辅助学习增强骨干网;通过在骨干网各级引入不同方向、不同频带的小波子带信息,可以使网络更加关注与分割任务相关的关键特征,实现更全面的特征提取。然后,设计了一种基于混合卷积的双自关注模块,该模块结合ConvMixer模块提取多种特征,从而增强了网络对不同尺度和形状的光学杯盘的适应能力。随后,双自注意模块在空间和通道两个维度上对特征进行处理,然后使用不同形式的卷积重新用于更深层次的特征提取。最后,利用特征映射乘法和跳跃连接来抑制无关特征,放大有价值的特征,并对视杯和视盘的整体分割结果进行细化。在Drishti-Gs和RIM-ONE-r3数据集上的实验结果表明,该方法优于目前大多数主流算法,并取得了更好的分割效果,特别是在光学杯和光盘区域边缘。
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来源期刊
Digital Signal Processing
Digital Signal Processing 工程技术-工程:电子与电气
CiteScore
5.30
自引率
17.20%
发文量
435
审稿时长
66 days
期刊介绍: Digital Signal Processing: A Review Journal is one of the oldest and most established journals in the field of signal processing yet it aims to be the most innovative. The Journal invites top quality research articles at the frontiers of research in all aspects of signal processing. Our objective is to provide a platform for the publication of ground-breaking research in signal processing with both academic and industrial appeal. The journal has a special emphasis on statistical signal processing methodology such as Bayesian signal processing, and encourages articles on emerging applications of signal processing such as: • big data• machine learning• internet of things• information security• systems biology and computational biology,• financial time series analysis,• autonomous vehicles,• quantum computing,• neuromorphic engineering,• human-computer interaction and intelligent user interfaces,• environmental signal processing,• geophysical signal processing including seismic signal processing,• chemioinformatics and bioinformatics,• audio, visual and performance arts,• disaster management and prevention,• renewable energy,
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