Advanced Biophotonics Laboratory, University of Massachusetts Lowell, Lowell, MA, USA.
Department of Radiation Medicine, MedStar Georgetown University Hospital, Washington, DC, USA.
Sci Rep. 2024 Jul 16;14(1):16389. doi: 10.1038/s41598-024-64855-2.
Fluorescence polarization (Fpol) imaging of methylene blue (MB) is a promising quantitative approach to thyroid cancer detection. Clinical translation of MB Fpol technology requires reduction of the data analysis time that can be achieved via deep learning-based automated cell segmentation with a 2D U-Net convolutional neural network. The model was trained and tested using images of pathologically diverse human thyroid cells and evaluated by comparing the number of cells selected, segmented areas, and Fpol values obtained using automated (AU) and manual (MA) data processing methods. Overall, the model segmented 15.8% more cells than the human operator. Differences in AU and MA segmented cell areas varied between - 55.2 and + 31.0%, whereas differences in Fpol values varied from - 20.7 and + 10.7%. No statistically significant differences between AU and MA derived Fpol data were observed. The largest differences in Fpol values correlated with greatest discrepancies in AU versus MA segmented cell areas. Time required for auto-processing was reduced to 10 s versus one hour required for MA data processing. Implementation of the automated cell analysis makes quantitative fluorescence polarization-based diagnosis clinically feasible.
甲川蓝（MB）荧光偏振（Fpol）成像术是一种很有前途的甲状腺癌检测定量方法。MB Fpol 技术的临床转化需要减少数据分析时间，这可以通过基于深度学习的二维 U-Net 卷积神经网络自动细胞分割来实现。该模型使用病理多样化的人类甲状腺细胞图像进行了训练和测试，并通过比较使用自动（AU）和手动（MA）数据处理方法获得的细胞数量、分割区域和 Fpol 值来进行评估。总的来说，该模型比人类操作者多分割了 15.8%的细胞。AU 和 MA 分割的细胞区域之间的差异在-55.2%到+31.0%之间，而 Fpol 值的差异在-20.7%到+10.7%之间。AU 和 MA 得出的 Fpol 数据之间没有观察到统计学上的显著差异。Fpol 值的最大差异与 AU 与 MA 分割的细胞区域之间的最大差异相关。自动处理所需的时间从 1 小时减少到 10 秒，而手动数据处理则需要 1 小时。自动细胞分析的实施使得基于定量荧光偏振的诊断在临床上成为可能。
