Deep Learning-Based TEM Image Analysis for Fully Automated Detection of Gold Nanoparticles Internalized Within Tumor Cell

Transmission electron microscopy (TEM) imaging can be used for detection/localization of gold nanoparticles (GNPs) within tumor cells. However, quantitative analysis of GNP-containing cellular TEM images typically relies on conventional/thresholding-based methods, which are manual, time-consuming, and prone to human errors. In this study, therefore, deep learning (DL)–based methods were developed for fully automated detection of GNPs from cellular TEM images. Several models of “you only look once (YOLO)” v5 were implemented, with a few adjustments to enhance the model’s performance by applying the transfer learning approach, adjusting the size of the input image, and choosing the best optimization algorithm. Seventy-eight original (12,040 augmented) TEM images of GNP-laden tumor cells were used for model implementation and validation. A maximum F1 score (harmonic mean of the precision and recall) of 0.982 was achieved by the best-trained models, while mean average precision was 0.989 and 0.843 at 0.50 and 0.50–0.95 intersection over union threshold, respectively. These results suggested the developed DL-based approach was capable of precisely estimating the number/position of internalized GNPs from cellular TEM images. A novel DL-based TEM image analysis tool from this study will benefit research/development efforts on GNP-based cancer therapeutics, for example, by enabling the modeling of GNP-laden tumor cells using nanometer-resolution TEM images.