I will present Spimagine, a python package to interactively visualize and process time lapsed volumetric data as generated with modern light sheet microscopes. The package provides a 3D+t data viewer as well as denoising and deconvolution methods and makes use of GPU acceleration via OpenCL.
During the last decade light sheet microscopy* has turned into the method of choice for live 3D imaging of dynamic biological processes. These microscopes generate data that has the potential to answer fundamental questions about an organisms development and morphogenesis. However one key challenge is to inspect and analyze this data, which typically amounts to several gigabytes of data per experiment.
Here I present Spimagine, a python package to interactively visualize and process such time lapsed volumetric data. The package leverages on the GPU via OpenCL to achieve high performance real time visualization/processing and is designed to help the research process by direct and interactive volume manipulation capabilities.
The package provides methods for GPU based volumetric and isosurface rendering of 3D+t data both via a standalone viewer as well as interactively e.g. from IPython. Animations can be easily created via key frame editing and exported to image sequences. Furthermore Spimagine includes several implementations of popular denoising and deconvolution algorithms and provides methods to calculate point spread functions routinely used in the bio image community. All processing methods are provided as fast GPU based implementations via PyOpenCL and can easily augment the viewer as plugins thus making it possible to study their impact on the data interactively.
The package is available at [http://github.com/maweigert/spimagine]((http://github.com/maweigert/spimagine)
*or SPIM = Selective plane illumination microscopy