Enabling Biological Discovery through Quantitative Microscopy
Image file reorganization and formatting.
Fragmenting, concatenating, changing dimensionality, changing type (e.g. bit depth) or converting image files from proprietary to open source formats are often necessary step for downstream analysis.
Information about genotype, treatment, scale, anatomy and microscopy parameters provide the biological and experimental context of the subsequent image analysis.
Segmentation can be manual or automated and defines biologically meaningful regions of interest (ROIs), such as tissues, cells and organelles and is a critical step in characterizing biological phenotypes.
Annotation, quantification and tracking of ROIs.
Determining spatial and temporal features of cells and other biological objects helps in the description of phenotypes resulting from genetic perturbation and drug treatments.
Machine Learning/Pattern recognition.
The use of artificial intelligence can help automate image analysis steps like segmentation or phenotypic classification.
3D rendering and projecting information gained from ROI analysis over images can help in the analysis of complex multi-dimensional data.
Data management and storage.
Dedicated file formats or the use of relational database software (e.g. MySQL) help to integrate pixel data, metadata and processed data to efficiently produce readouts for image interpretation.
Statistical tests and Charting.
Integrating statistics and charting tools into custom systems, as opposed to using external tools, greatly enhances the productivity of quantitative microscopy.