UPBAS-Net:一个上采样供电的边界感知分割网络,用于显微镜图像中的荧光点。

IF 6.7 1区 化学 Q1 CHEMISTRY, ANALYTICAL
Huan Liu, , , Lu Huang, , , Jiahui Wang, , , Jintao Hu, , , Xinyin Li, , , Yingying Guo, , , Feng Chen*, , and , Yongxi Zhao*, 
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引用次数: 0

摘要

显微镜细胞图像中荧光点的准确检测和分割仍然具有挑战性。传统的方法,基于质心定位或逐像素语义分割,往往不能描绘出单个点的边界。这一限制极大地阻碍了形态异质性的定量分析和密集分布的亚细胞信号的解释。在这里,我们提出了UPBAS-Net,这是一个统一的计算框架,将基于傅里叶插值的预处理与增强的YOLOv8架构集成在一起,该架构包含一个额外的上采样层,以改进浅层特征提取,并实现亚像素分辨率下荧光点的边界感知实例分割。它克服了传统质心定位和逐像素分类的局限性,能够准确地描绘点边界。实验结果表明,与deepBlink模型相比,UPBAS-Net在多个基准数据集上的点定位精度提高了8.27%。此外,它还具有出色的可扩展性,可以同时分割荧光点和细胞边界,从而实现单细胞分辨率的综合空间相关性分析。此外,我们提供了一个用户友好的基于web的分析平台,具有容器化的工作流程管理,使非程序员能够使用预训练的模型执行自动化的斑点和细胞分割。该平台可在http://cellpropack.com/免费访问。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

UPBAS-Net: An Upsampling-Powered Boundary-Aware Segmentation Network for Fluorescent Spots in Microscopy Images

UPBAS-Net: An Upsampling-Powered Boundary-Aware Segmentation Network for Fluorescent Spots in Microscopy Images

Accurate detection and segmentation of fluorescent spots in microscopy cell images remain challenging. Traditional methods, based on centroid localization or pixel-wise semantic segmentation, often fail to delineate individual spot boundaries. This limitation significantly hinders the quantitative analysis of morphological heterogeneity and the interpretation of densely distributed subcellular signals. Here, we propose UPBAS-Net, a unified computational framework that integrates Fourier interpolation-based preprocessing with an enhanced YOLOv8 architecture incorporating an additional upsampling layer to improve shallow feature extraction and enable boundary-aware instance segmentation of fluorescent spots at subpixel resolution. It overcomes the limitations of traditional centroid localization and pixel-wise classification, enabling accurate delineation of spot boundaries. Experimental results show that UPBAS-Net achieves substantial improvements in spot localization accuracy, with F1-score gains up to 8.27% compared to the deepBlink model across multiple benchmark data sets. Furthermore, it demonstrates excellent scalability with the simultaneous segmentation of fluorescent spots and cellular boundaries, enabling integrated spatial correlation analysis at single-cell resolution. Additionally, we provide a user-friendly web-based analytical platform with containerized workflow management, enabling nonprogrammers to perform automated spot and cell segmentation using pretrained models. The platform is freely accessible at http://cellpropack.com/.

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来源期刊
Analytical Chemistry
Analytical Chemistry 化学-分析化学
CiteScore
12.10
自引率
12.20%
发文量
1949
审稿时长
1.4 months
期刊介绍: Analytical Chemistry, a peer-reviewed research journal, focuses on disseminating new and original knowledge across all branches of analytical chemistry. Fundamental articles may explore general principles of chemical measurement science and need not directly address existing or potential analytical methodology. They can be entirely theoretical or report experimental results. Contributions may cover various phases of analytical operations, including sampling, bioanalysis, electrochemistry, mass spectrometry, microscale and nanoscale systems, environmental analysis, separations, spectroscopy, chemical reactions and selectivity, instrumentation, imaging, surface analysis, and data processing. Papers discussing known analytical methods should present a significant, original application of the method, a notable improvement, or results on an important analyte.
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