Classification of organelle objects using high resolution imaging and machine learning in 2D and 3D cancer cell systems

Balance between the cell viability and death in 3D

“…The intrinsic disadvantages of biosensors, such as the cell-dependent staining are being addressed via continued research in chemistry and material sciences, biofabrication methods, and the introduction of novel non-invasive optical imaging techniques, such as near-infrared II light-sheet microscopy [152,224,225], potentially using single cell analysis approaches merged with high-content [173,174] and FLIM-based multiplexing [152,226,227]. Essentially, combining mentioned analytical chemistry, biomedical optics and advanced cell biology techniques must be logically integrated with machine learning and advanced modeling approaches [108,[228][229][230][231][232] to further address the viability, reproducibility, and 'sustainability' of the popular 3D tissue models.…”

Probing organoid metabolism using fluorescence lifetime imaging microscopy (FLIM): The next frontier of drug discovery and disease understanding

“…Furthermore; another extension of this AI subset; convolutional neural networks (CNN) have demonstrated applicability using FLIM data and micrographs with accurate cell tracking and classification performed [261]; and have been able to predict FLIM images based on fluorescent data when trained using a small subset of FLIM images; with a high degree of accuracy. Such applications are rapidly gathering momentum and are at the cutting edge of the field [97,271,272]. (include that of organoids) and ensuring multimodal or at least spatial tracking between modalities is key to deriving clear correlations.…”