Inference on volumetric data

To get started, download an example HeLa cell FIBSEM dataset.

If you installed napari into a virtual environment as suggested in Installation, be sure to activate it:

conda activate empanada

Launch napari:

napari

Loading HeLa Cell Data

Drag and drop the hela_cell_em.tif file into the napari window.

Parameter Testing

Before running 3D inference, which may take a considerable amount of time for large volumes, using the 2D Inference (Parameter Testing) for parameter testing. For more details see Tuning downsampling and Choosing the right model. In depth descriptions of how the other parameters affect model output are provided in Inference Best Practices.

It’s important to test the model on all three principle planes: xy, xz, and yz in order to check if ortho-plane inference or stack inference on a particular plane would be better. Click the transpose button (red arrow below) to view and test models on different planes.

By default, xy planes are shown. One click of transpose will show yz planes, two clicks will show xz planes and three clicks will bring it back to xy planes. After each transpose, run the 2D Inference module and inspect results:

Here, the results are equally good on all three planes and the voxels are clearly isotropic. That means this dataset is a good candidate for ortho-plane inference.

Running 3D Inference

All parameters and best practices for setting them are detailed in 3D Inference and Inference Best Practices, respectively. We’ll run ortho-plane inference with the parameters shown below.

Note

For large datasets, it’s recommended to start by running 3D inference on a small ROI of 256x256x256 or similar. Tweak parameters to get satisfactory results on this ROI before applying to the larger dataset.

The consensus algorithm used to merge the xy, yz, and xz segmentation stacks can struggle when challenged with very closely packed together objects. If results look satisfactory with stack inference on the chosen ROI, then it’s recommended to avoid ortho-plane inference. Checking the box to “Return xy, xz, and yz stacks” gives you the option to choose between any of the stacks or ortho-plane results without re-running inference. Note, however, that the stack inference results do NOT have small object filtering applied so may show more false positives than if they were generated outside of the ortho-plane inference workflow.

Visualizing the results

Results can be visualized in 3D by toggling the 3D viewer (red arrow). Turn on and off the stack inference results and compare them to ortho-plane results.

Proofreading in 3D

The proofreading operations for 3D data work identically to those for Proofreading in 2D. Simply check the “Apply in 3D” option to merge, split, and delete labels throughout the entire volume.

Note

If you chose to save the segmentations as zarr the split proofreading function will not work. We plan to address this gap in the future.

Exporting 3D

To save, simply select one or more layers and “Save selected layers”:

or use the Export Segmentations module and select 3D image as the Export type.

Note

If you chose to save the segmentations as zarr, there’s no need to export them – they’re already saved in the directory you picked.