@article {70417, title = {Remotely accessible optical microscopy education through a wide (and global) lens.}, journal = {J Microsc}, year = {2026}, month = {2026 May 15}, abstract = {

Access to rigorous optical microscopy education remains unevenly distributed across the globe despite widespread use of optical methods. While lower-resourced settings certainly feel this burden, it is far from a foregone conclusion that such opportunities are ubiquitous, even at well-funded institutions. Despite a growing number of online educational resources and other tools, many biomedical researchers learn microscopy in a task-specific manner, and without the conceptual foundation to maximise the potential of its capabilities, robustly interpret data, avoid bias, and/or troubleshoot effectively. Given microscopy{\textquoteright}s significant impact in the discovery process and status as a cornerstone scientific tool, we developed a remotely accessible, global, and highly interactive program to help address the education gap with respect to the fundamentals of microscopy, irrespective of one{\textquoteright}s location or ability to access high-end platforms. Here we reflect on the design and evolution of the resulting {\textquoteright}Widening the Lens{\textquoteright} (WtL) program: what worked, what didn{\textquoteright}t, and how the course adapted in response, with the intent of enabling others to leverage our experience to develop programs of their own. We also describe our partnership with critical network organisations, including the African BioImaging Consortium (ABIC) to implement a WtL based train-the-trainer model across the African continent. WtL demonstrates that equitable microscopy education is not only achievable, but that closing education gaps involves building a community in concert with likeminded educational opportunities and networks across the globe.

}, issn = {1365-2818}, doi = {10.1111/jmi.70112}, url = {https://doi.org/10.1111/jmi.70112}, author = {Sanchez, Zachary and de Jesus, Miguel and David, Moses and Taheri, Ali and Olatunji, Sunday Yinka and Rahmoon, Mai and Eliceiri, Kevin W and Mahadevan-Jansen, Anita and Millis, Bryan A} } @article {48367, title = {Trainable Weka Segmentation: a machine learning tool for microscopy pixel classification.}, journal = {Bioinformatics (Oxford, England)}, volume = {33}, year = {2017}, month = {2017 Mar 29}, pages = {2424-6}, abstract = {

Summary: State-of-the-art light and electron microscopes are capable of acquiring large image datasets, but quantitatively evaluating the data often involves manually annotating structures of interest. This processis time-consuming and often a major bottleneck in the evaluation pipeline. To overcome this problem, we have introduced the Trainable Weka Segmentation (TWS), a machine learning tool that leveragesa limited number of manual annotations in order to train a classifier and segment the remaining dataautomatically. In addition, TWS can provide unsupervised segmentation learning schemes (clustering) and can be customized to employ user-designed image features or classifiers.

Availability and Implementation: TWS is distributed as open-source software as part of the Fiji image processing distribution of ImageJ at http://imagej.net/Trainable_Weka_Segmentation.

Contact: ignacio.arganda@ehu.eus.

Supplementary information: Supplementary data are available at Bioinformatics online.

}, issn = {1367-4811}, doi = {10.1093/bioinformatics/btx180}, author = {Arganda-Carreras, Ignacio and Kaynig, Verena and Rueden, Curtis and Eliceiri, Kevin W and Schindelin, Johannes and Cardona, Albert and Seung, H Sebastian} } @article {4984, title = {Biological imaging software tools.}, journal = {Nature Methods}, volume = {9}, year = {2012}, month = {2012 Jun 28}, pages = {697-710}, abstract = {

Few technologies are more widespread in modern biological laboratories than imaging. Recent advances in optical technologies and instrumentation are providing hitherto unimagined capabilities. Almost all these advances have required the development of software to enable the acquisition, management, analysis and visualization of the imaging data. We review each computational step that biologists encounter when dealing with digital images, the inherent challenges and the overall status of available software for bioimage informatics, focusing on open-source options.

}, issn = {1548-7105}, doi = {10.1038/nmeth.2084}, author = {Eliceiri, Kevin W and Berthold, Michael R and Goldberg, Ilya G and Ib{\'a}{\~n}ez, Luis and Manjunath, B S and Martone, Maryann E and Murphy, Robert F and Peng, Hanchuan and Plant, Anne L and Roysam, Badrinath and Stuurmann, Nico and Swedlow, Jason R and Tomancak, Pavel and Carpenter, Anne E.} }