Introducing 4D datasets for time series in WEBKNOSSOS
WEBKNOSSOS just added support for time series datasets, opening up a new dimension in your data exploration. Now, you can not only view your data in 3D but also in 4D, adding the element of time. Time series volumetric microscopy is an exciting technique that enables observations of dynamic processes within living organisms, as commonly found in confocal microscopy, two-photon microscopy, fluorescence microscopy, and light sheet microscopy.
Let’s look into the process of using time series in WEBKNOSSOS. First, you need to import a 4D dataset. For this example, we will start by streaming a Zarr dataset from Payne et al. published in Science in 2020. You can access the Zarr link here to stream the dataset. Alternatively, you can open the imported dataset directly in WEBKNOSSOS.
Once the dataset is loaded, you can navigate through the data at different points in time using the slider located in the top bar. You can continue to explore the other three dimensions of the data for each time point. As usual, you can move across the XY plane, zoom in and out, and scroll through the Z direction.
Now, you can begin annotating your 4D data. Click on “create an annotation”. At t = 8, use the annotation tools like the brush to segment a cell. Annotate the cell across different slices, forming a 3D segment.
Now, set the time slider to t = 7. Repeat this process, and voila! You have your cell segment in 3D, captured at various moments in time.
WEBKNOSSOS not only supports 4D datasets but can also handle N-dimensional datasets. We would appreciate your feedback on this feature and any suggestions for improvement. Furthermore, support for this feature will be available in our Python libraries soon.
