Fluorescence lifetime image microscopy prediction with convolutional neural networks for cell detection and...

Area of Science:

• Computational pathology
• Biomedical imaging
• Machine learning

Background:

• Deep learning models, particularly CNNs, excel at analyzing microscopy images for cellular and tissue analysis.
• Traditional microscopy (brightfield, fluorescence) is common due to large datasets, but advanced techniques may offer improved performance.
• FLIM offers unique data but its application with CNNs for histopathology is underexplored.

Purpose of the Study:

• To evaluate the efficacy of CNNs for cell detection and classification using FLIM data.
• To investigate if generated FLIM images can enhance CNN performance in computational histopathology.
• To assess the data requirements for training CNNs with FLIM data.

Main Methods:

• Applied CNNs to histology images acquired via FLIM for cell detection and classification.
• Utilized a dense U-Net CNN to generate FLIM images from standard fluorescence images.
• Compared CNN performance using FLIM, fluorescence images, and generated FLIM images as input.

Main Results:

• CNNs achieved high accuracy in cell detection and classification with FLIM data, outperforming traditional fluorescence images.
• Pretrained CNNs showed improved accuracy on FLIM data, even with limited datasets.
• Generated FLIM images closely resembled ground truth and significantly improved cell detection/classification accuracy when used with various CNNs.

Conclusions:

• FLIM data enables high-accuracy CNN-based cell analysis in histopathology with minimal training data.
• Generating FLIM images from fluorescence data via U-Net CNNs is feasible and enhances downstream computational pathology tasks.
• This approach offers a promising strategy for improving automated analysis in histopathology, even with limited ground-truth FLIM data.