Key Laboratory of Advanced Manufacturing and Automation Technology(Guilin University of Technology), Education Department of Guangxi Zhuang Autonomous Region, Guilin, 541006, China.
College of Mechanical and Control Engineering, Guilin University of Technology, Guilin, 541006, China.
Anal Methods. 2024 Oct 10;16(39):6715-6725. doi: 10.1039/d4ay01187b.
The rising demand for point-of-care testing (POCT) in disease diagnosis has made LFIA sensors based on dendritic metal thin film (HD-nanometal) and background fluorescence technology essential for rapid and accurate disease marker detection, thanks to their integrated design, high sensitivity, and cost-effectiveness. However, their unique 3D nanostructures cause significant fluorescence variation, challenging traditional image processing methods in segmenting weak fluorescence regions. This paper develops a deep learning method to efficiently segment target regions in HD-nanometal LFIA sensor images, improving quantitative detection accuracy. We propose an improved UNet++ network with attention and residual modules, accurately segmenting varying fluorescence intensities, especially weak ones. We evaluated the method using IoU and Dice coefficients, comparing it with UNet, Deeplabv3, and UNet++. We used an HD-nanoCu-Ni LFIA sensor for cardiac troponin I (cTnI) as a case study to validate the method's practicality. The proposed method achieved a 96.3% IoU, outperforming other networks. The between characteristic quantity and cTnI concentration reached 0.994, confirming the method's accuracy and reliability. This enhances POCT accuracy and provides a reference for future fluorescence immunochromatography expansion.
由于集成设计、高灵敏度和成本效益，基于树突状金属薄膜（HD-纳米金属）和背景荧光技术的 LFIA 传感器在疾病诊断中的即时检测需求不断增长，对于快速、准确的疾病标志物检测至关重要。然而，其独特的 3D 纳米结构导致荧光变化显著，给传统的弱荧光区域分割图像处理方法带来挑战。本文提出了一种深度学习方法，用于高效分割 HD-纳米金属 LFIA 传感器图像中的目标区域，提高定量检测精度。我们提出了一种带有注意力和残差模块的改进型 UNet++网络，能够准确分割不同的荧光强度，尤其是较弱的荧光强度。我们使用 IoU 和 Dice 系数对该方法进行了评估，并与 UNet、Deeplabv3 和 UNet++进行了比较。我们使用 HD-nanoCu-Ni LFIA 传感器作为心脏肌钙蛋白 I（cTnI）的案例研究来验证该方法的实用性。所提出的方法实现了 96.3%的 IoU，优于其他网络。特征量与 cTnI 浓度之间的相关性达到 0.994，证实了该方法的准确性和可靠性。这提高了 POCT 的准确性，并为未来的荧光免疫层析扩展提供了参考。
