Microaneurysm Segmentation Method for Diabetic Retinopathy Fundus Lesions Based on the Multi-Scale Feature Subtraction Fusion Network

IF 2.5 4区计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

International Journal of Imaging Systems and Technology Pub Date : 2025-06-18 DOI:10.1002/ima.70140

Jie Zhang, Xiangyu Jiang, Shuting Ni, Shuang Liu, Wei Zou

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Abstract

Detecting and segmenting microaneurysms can help doctors diagnose the condition and formulate subsequent treatment plans. A multi-scale feature subtraction fusion network is proposed in this paper. It includes two modules: the multi-scale feature compensation module and the subtraction fusion module. In the multi-scale feature compensation module, the features between adjacent levels of the network are fused. Considering that simply concatenating features may lead to feature redundancy, a subtraction fusion module is designed. To enable the neural network to extract more detailed information, a branch is introduced. A wavelet attention enhancement module is designed to transform the channel attention of frequency coefficients extracted by wavelet transform. The proposed method can help the network learn feature diversity better, and hence can improve segmentation performance. Experimental results show that, as compared to the existing methods, the proposed method can achieve better performance with Dice coefficients of 0.4481, 0.4860, and 0.3561 on the IDRID, E-Ophtha, and DDR datasets, respectively.

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基于多尺度特征减法融合网络的糖尿病视网膜病变眼底微动脉瘤分割方法

检测和分割微动脉瘤可以帮助医生诊断病情并制定后续治疗计划。提出了一种多尺度特征减法融合网络。它包括两个模块：多尺度特征补偿模块和减法融合模块。在多尺度特征补偿模块中，融合了网络相邻层之间的特征。针对简单拼接特征可能导致特征冗余的问题，设计了减法融合模块。为了使神经网络能够提取更详细的信息，引入了一个分支。设计了小波注意增强模块，对小波变换提取的频率系数的信道注意进行变换。该方法可以帮助网络更好地学习特征多样性，从而提高分割性能。实验结果表明，与现有方法相比，该方法在IDRID、E-Ophtha和DDR数据集上的Dice系数分别为0.4481、0.4860和0.3561，可以获得更好的性能。

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来源期刊

International Journal of Imaging Systems and Technology 工程技术-成像科学与照相技术

CiteScore

6.90

自引率

6.10%

发文量

138

审稿时长

3 months

期刊介绍： The International Journal of Imaging Systems and Technology (IMA) is a forum for the exchange of ideas and results relevant to imaging systems, including imaging physics and informatics. The journal covers all imaging modalities in humans and animals. IMA accepts technically sound and scientifically rigorous research in the interdisciplinary field of imaging, including relevant algorithmic research and hardware and software development, and their applications relevant to medical research. The journal provides a platform to publish original research in structural and functional imaging. The journal is also open to imaging studies of the human body and on animals that describe novel diagnostic imaging and analyses methods. Technical, theoretical, and clinical research in both normal and clinical populations is encouraged. Submissions describing methods, software, databases, replication studies as well as negative results are also considered. The scope of the journal includes, but is not limited to, the following in the context of biomedical research: Imaging and neuro-imaging modalities: structural MRI, functional MRI, PET, SPECT, CT, ultrasound, EEG, MEG, NIRS etc.; Neuromodulation and brain stimulation techniques such as TMS and tDCS; Software and hardware for imaging, especially related to human and animal health; Image segmentation in normal and clinical populations; Pattern analysis and classification using machine learning techniques; Computational modeling and analysis; Brain connectivity and connectomics; Systems-level characterization of brain function; Neural networks and neurorobotics; Computer vision, based on human/animal physiology; Brain-computer interface (BCI) technology; Big data, databasing and data mining.