The field of biological imaging has evolved considerably during the past decade as a result of recent (r)evolutions in fluorescence labeling and optical microscopy. Bioimage informatics has been identified as a top priority to cope with the ever-increasing amount of microscopy data. The challenges and opportunities for researchers in image and signal processing are manyfold. They span the areas of mathematical imaging, with problems such as denoising, 3-D deconvolution and super-resolution localization, as well as image analysis for the segmentation, detection and recognition of biological structures in 3-D. The dynamic aspect of the data requires the development of novel algorithms for tracking fluorescent particles and analyzing high-throughput microscopy data (labeling of cells, phenotyping, extraction of gene expression profiles). A crucial aspect of bioimage informatics is making image analysis tools available to biologists so that they can be applied to real data and used on a routine basis. Developers may benefit from open-source frameworks and international initiative such as OBIA for easying-up this process and creating collaboration networks with biologists.
Claudio Bruschini, Edoardo Charbon, Arin Can Ülkü, Yichen Feng
Xin Yang, Bo Wang, Yixin Wang, Jun Ma, Lin Han, Maxime Emmanuel Scheder, Marco Labagnara, Sahand Jamal Rahi, Vojislav Gligorovski, Yao Zhang