A Modular Suite of Advanced Bioimaging Tools with scikit-image and Dash

scikit-image is the open-source image processing toolkit of Scientific Python. It proposes a collection of algorithms which address the various image processing tasks encountered in science (denoising, segmentation, feature extraction…). scikit-image is application-agnostic and its algorithms accept both two-dimensional and three-dimensional (sometimes n-d) images for compatibility with the various image modalities (microscopy, tomography, MRI, etc.) of science. However, it is a core dependence of many application-specific image processing packages such as CellProfiler or hyperspy. scikit-image targets a wide community of students, engineers and scientists, many of them self-taught about image processing. An extensive documentation is found on the project website, including some narrative documentation and “getting started” instructions, and also a popular gallery of examples illustrating the use of scikit-image algorithms.

Dash is an open-source framework that empowers data scientists to create interactive web applications declaratively in pure Python or R (no JavaScript required). The Dash library allows developer-users to write modular GUI components (e.g., sliders, dropdowns, tables, dials) for their analytics code. It is based on React JavaScript and Flask. Labs and R&D teams find that Dash’s trade-off between ease of use and high-quality, reactive user interfaces meets their needs, and they are able to deploy apps that are highly portable and shareable, bringing analytics and data science work out of the silo and into the wider organization.