Data Augmentation is quintessential for training modern AI-based image analysis methods. Through careful modification of the available training data, the training distribution is extended and/or covered in more detail, reducing the potential for overfitting. Batchgenerators …

Introduction to Biomedisa

Biomedisa is a free and easy-to-use open-source application designed for segmenting large 3D volumetric images, such as CT and MRI scans. Developed at The Australian National University's Computational Biology and Medical Imaging …

Drishti is an open-source scientific visualisation software designed by Ajay Limaye at the National Computational Infrastructure's VizLab.

 

The central idea of Drishti is that scientists can use it to explore and present volumetric datasets without …

With the EM glossary initiative, our goal is to harmonize terminology in the field of electron and ion microscopy so humans, devices and algorithms can exchange data easily and collaborate more efficiently and effectively.

We …

When confronted with a new dataset, the application of AI-based segmentation methods is non-trivial. Substantial manual effort is required to optimize the network topology, preprocessing, training procedure and inference strategy to the task at hand. …

This python based tool extracts metadata from machine generated scanning microscopy images in the TIFF format. Currently only metadata from SEMs manufactured by Zeiss, and acquired by their own software is supported. In future, metadata …