This template for open-source development of software has provided opportunities for image analysis to add considerably to translational research by enabling the development of the bespoke analytical methods required to address specific and emerging needs, which are often beyond the scope of existing commercial applications 7. These open source packages encourage users to engage in further development and sharing of customized analysis solutions in the form of plugins, scripts, pipelines or workflows – enhancing the quality and reproducibility of research, particularly in the fields of microscopy and high content imaging. Led by ImageJ 3, researchers in multiple disciplines can now choose from a selection of powerful tools, such as Fiji 4, Icy 5, and CellProfiler 6, to perform their image analyses. In recent years, a vibrant ecosystem of open source bioimage analysis software has developed.

New and powerful software tools are urgently required to ensure that pathological assessment of tissue is practical, accessible and reliable for biological discovery and the development of clinically-relevant tissue diagnostics. Manual subjective scoring of this data by traditional pathologist assessment is no longer sufficient to support large-scale tissue biomarker trials, and cannot ensure the high quality, reproducible, objective analysis essential for reliable clinical correlation and candidate biomarker selection. Whole slide scanners can rapidly generate ultra-large 2D images or z-stacks in which each plane may contain up to 40 GB uncompressed data. This area has become widely known as digital pathology 1, 2. The ability to acquire high resolution digital scans of entire microscopic slides with high-resolution whole slide scanners is transforming tissue biomarker and companion diagnostic discovery through digital image analytics, automation, quantitation and objective screening of tissue samples.

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