EF-net:准确的边缘细分用于从CT图像中细分COVID-19肺部感染
1University of New South Wales, Australia.
Heliyon
|December 13, 2024
概括
一个新的深度学习模型,基于边缘的双平行注意力 (EDA) 引导的特征过网络 (EF-Net),精确地细分COVID-19肺部病变. 这种先进的网络改善了边界检测,以便在CT扫描中更准确地识别病变.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 在CT扫描中精确细分COVID-19肺部病变,由于界限模糊和对比度低,因此具有挑战性.
- 现有的模型在精确识别和细分病变边缘方面扎.
研究的目的:
- 引入一种新的深度学习模型,即基于边缘的双平行注意力 (EDA) 引导的特征过网络 (EF-Net),用于准确的COVID-19病变细分.
- 为了提高病变边界的精确识别,提高细分质量.
主要方法:
- 拟议的EF-Net模型集成了一个EDA模块来提取结构和纹理特征和一个特征过模块 (FFM).
- 该EDA模块使用低级特征识别损伤边界.
- 该FFM融合了含义丰富的深层特征与EDA提取的纹理和轮信息通过一个门机制.
主要成果:
- 与Inf_Net,GFNet和BSNet相比,EF-Net在细分COVID-19肺病变方面表现优越.
- 该模型在三个不同的数据集中实现了98.1%,97.3%和72.1%的高子系数.
- EF-Net提供了更清晰的细分结果,特别是在损伤边缘.
结论:
- EF-Net模型在准确细分COVID-19肺病变方面取得了重大进展.
- 它精确识别损伤边缘的能力提高了医学成像诊断能力.
- 拟议的网络显示了CT扫描中肺部感染的自动化分析的有希望的结果.
相关概念视频
01:18Effects of EDTA on End-Point Detection Methods
829
Different methods, such as visual observance of metal-ion indicators, spectroscopic techniques, and potentiometric methods, can determine the endpoint of an EDTA titration.
In the visual method, metal-ion indicators (metallochromic dyes), which have distinct colors in their free and complex forms, are added to the mixture to signal the titration's end point. They form stable complexes with metal ions, but these complexes are weaker than the corresponding metal–EDTA complexes. As a...
In the visual method, metal-ion indicators (metallochromic dyes), which have distinct colors in their free and complex forms, are added to the mixture to signal the titration's end point. They form stable complexes with metal ions, but these complexes are weaker than the corresponding metal–EDTA complexes. As a...
829
01:28Imaging Studies for Cardiovascular System V: CT
674
Cardiac computed tomography (CT) scanning is an advanced cardiac imaging technique that utilizes CT technology, with or without intravenous (IV) contrast, to produce accurate cross-sectional virtual slices of specific areas of the heart, coronary circulation, and major blood vessels such as the aorta, pulmonary veins, and arteries. The computer processes these slices to generate three-dimensional images. Multidetector CT (MDCT) is a rapid form of CT scanning that captures multiple slices...
674