Bioimage analysis is a critically needed discipline, especially as the number of new tools is exploding as part of the “deep learning revolution” and as larger data sizes and automated screening systems make manual data handling of microscopy data increasingly impractical. While there are more educational and training materials than ever, a few major challenges still exist:
- Bioimage analysis is still often seen as an “add-on” - not required activity, meaning researchers need to learn it on their own rather than having it included as part of general graduate education and/or microscopy education.
- A large number of bioimage analysis resources now exist including open source software software, training materials and workshops, but without community standards and recommendations many of these are of uncertain quality. Complicating existing efforts to track these comprehensively, bioimage analysis tools are constantly evolving - some become unmaintained and unusable, while others add new functionalities and/or update interfaces for interaction. This gives many materials a short “shelf life” and makes it difficult to determine what is still useful, even for experts but especially for non-experts.
- The community of users covers a wide range of experience levels and computational comfort levels, meaning some materials must have multiple versions created to reach the entire community.