Utilizing convolutional neural networks for discriminating cancer and stromal cells in three-dimensional cell culture images with nuclei counterstain.

ABSTRACT:

Significance

Accurate cell segmentation and classification in three-dimensional (3D) images are vital for studying live cell behavior and drug responses in 3D tissue culture. Evaluating diverse cell populations in 3D cell culture over time necessitates non-toxic staining methods, as specific fluorescent tags may not be suitable, and immunofluorescence staining can be cytotoxic for prolonged live cell cultures.

Aim

We aim to perform machine learning-based cell classification within a live heterogeneous cell culture population grown in a 3D tissue culture relying only on reflectance, transmittance, and nuclei counterstained images obtained by confocal microscopy.

Approach

In this study, we employed a supervised convolutional neural network (CNN) to classify tumor cells

SUBMITTER: Nguyen HT 

PROVIDER: S-EPMC11344342 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature