The algorithms described on this page are used to generate the 3D morphology image in the output bundle: morphology_3d/ch0000_dapi_3d.ome.tif.

Images are captured at the same coordinates across cycles through integrated hardware and software tissue tracking. The instrument then identifies the upper bound of the tissue section to find the location of the first Z-slice. These pieces of information collectively determine the range of Z-slices to image. This process has been robust to all tested sections and tissue types.

The DAPI image process algorithm produces a complete 3D morphology image for the region of interest. The 3D image is the reference image volume for segmenting cells and assigning transcripts to cells.

First, field curvature (axial) and lateral distortion are computationally corrected in the internal sensor data using pre-collected channel-specific instrument calibrations. This also compensates for axial and lateral chromatic aberration between channels. Next, the Z-stacks from internal sensor data (0.75 µm step size) are further subsampled to a 3 µm step size. This subsampling step size was determined empirically to be a useful resolution for cell segmentation quality.

Shared image features are then extracted from the regions where FOVs overlap. Feature matching is performed to estimate the offsets between adjoining FOVs. The offsets are used to ensure consistent alignment across the image (global alignment).

Finally, the 3D DAPI image volumes (Z-stacks) generated across FOVs are blended together to construct a stitched volume that is used for nucleus segmentation.