The research conducted in the Jug Group is pushing the boundary of what image analyses and machine learning can do for quantifying biological (image) data. The common denominator of such projects is the indisputable necessity to analyse large amounts of light microscopy data without causing impossible amounts of manual data curation and data processing to life-science researchers (aka our users and collaborators).
From a computational point of view we are interested in (i) image denoising and restoration, (ii) object segmentation and tracking, and (iii) analysis modules for tasks such as object detection, nD image registration/ deformation, etc.
Next to developing novel machine learned and algorithmic solutions, the Jug Group also has a strong emphasis on casting these methods into modular and reusable software packages. This work happens to a large degree in the context of Fiji and the trending scientific Python world. We strongly believe that the power and flexibility of the image.sc community and their very rich universe of open source solutions is of a value and utility to the life-science community at large that can hardly be underestimated.