GR-Net: Gated axial attention ResNest network for polyp segmentation

IF 3 4区计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC International Journal of Imaging Systems and Technology Pub Date : 2023-04-13 DOI:10.1002/ima.22887

Shen Jiang, Jinjiang Li, Zhen Hua

引用次数: 0

Abstract

Medical image segmentation is a key step in medical image analysis. The small differences in the background and foreground of medical images and the small size of most medical data sets make medical segmentation difficult. This paper uses a global-local training strategy to train the network. In the global structure, ResNest is used as the backbone of the network, and parallel decoders are added to aggregate features, as well as gated axial attention to adapt to small datasets. In the local structure, the extraction of image details is accomplished by dividing the images into equal patches of the same size. To evaluate the performance of the model, qualitative and quantitative comparisons were performed on five datasets, Kvasir-SEG, CVC-ColonDB, CVC-ClinicDB, CVC-300, and ETIS-LaribPolypDB, and the segmentation results were significantly better than the current mainstream polyp segmentation methods. The results show that the model has better segmentation performance and generalization ability.

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GR-Net：用于息肉分割的门控轴向注意力ResNest网络

医学图像分割是医学图像分析的关键步骤。医学图像背景和前景的微小差异以及大多数医学数据集的小尺寸使得医学分割变得困难。本文采用全局-局部训练策略对网络进行训练。在全局结构中，ResNest被用作网络的主干，并添加了并行解码器来聚合特征，以及门控轴向注意力以适应小数据集。在局部结构中，图像细节的提取是通过将图像划分为相同大小的相等块来实现的。为了评估模型的性能，在Kvasir SEG、CVC ColonDB、CVC ClinicDB、CVC-300和ETIS LaribPolypDB五个数据集上进行了定性和定量比较，分割结果明显优于当前主流的息肉分割方法。结果表明，该模型具有较好的分割性能和泛化能力。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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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.