Deep Learning-Based TEM Image Analysis for Fully Automated Detection of Gold Nanoparticles Internalized Within Tumor Cell.
Amrit KaphleSandun JayarathnaHem MoktanMaureen AliruSubhiksha RaghuramSunil KrishnanSang Hyun ChoPublished in: Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada (2023)
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.
Keyphrases
- deep learning
- convolutional neural network
- gold nanoparticles
- artificial intelligence
- machine learning
- endothelial cells
- healthcare
- loop mediated isothermal amplification
- primary care
- real time pcr
- single cell
- quality improvement
- squamous cell carcinoma
- emergency department
- label free
- stem cells
- papillary thyroid
- mesenchymal stem cells
- patient safety
- cell therapy
- resistance training
- squamous cell
- body composition
- sensitive detection
- photodynamic therapy
- virtual reality
- high intensity