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Enabling Biological Discovery through Quantitative Microscopy
Microscopic imaging of tissues, cells and molecules has always been a powerful discovery tool in biological research. Traditionally, analysis of biological images was mainly based on visual inspection by the human experimentalist. In recent years, the increasing automation of microscopy has led to an explosion of digital image data that drive the development of new computational methods for image quantification and visualization.

BioImagingMW provides customized solutions for image analysis to help biomedical researchers gain useful and relevant information from microscopic images. Customized solutions or systems can be tailored to specific image types, analysis tasks and desired outputs. Microscopic images are extremely diverse thanks to a multitude of experimental parameters, such as different biological problems, models, tissues, cells, phenotypes, imaging modalities, labeling techniques (GFP, antibodies etc.), dimensionalities (2D, 3D, time-lapse) and  file formats. Equally diverse are the requirements for image quantification, phenotypic characterization, data management and visualization. As such, customized image analysis system that are built by integrating functional modules from different software libraries with a dedicated user interface can be more productive and accurate in performing a particular task than generic image analysis packages.
Customized Image Analysis Pipelines

BioImagingMW designs and implements image analysis systems that integrate combinations of different modules based on user requirements into custom pipelines. Computational tools are implemented as standalone applications in Java or C++ using open-source software libraries. Examples of computational tools for image analysis developed by Martin Wasser and group members in academic research are featured here.
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.
Image Annotation.
Information about genotype, treatment, scale, anatomy and microscopy parameters provide the biological and experimental context of the subsequent image analysis. 
Image Segmentation.
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.
Image Visualization.
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.

Biological Discovery through
Quantitative Microscopy
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