Parameter Reference

Here we provide parameter options for each workflow in MIAAIM. Commands are added in the

Note

For up-to-date references of parameters for each workflow, visit the Workflow GitHub Repositories

HDIprep

Here we list the optional input parameters that can be supplied to the HDIprep workflow through the YAML parameter file. These parameters are passed to both the fixed and moving images with separate YAML files.

–fixed-pars and –moving-pars
YAML Step / Flag	Description	Options
Step : `ImportOptions`	options for reading image data
`flatten:`	flatten pixels for dimension reduction	Options: `True` if compressing images `False` if histology processing

`subsample:`	subsample image for compression	Options: `True` if subsampling pixels `False` if no subsampling

`method:`	subsampling method	Options: `'grid'` for uniform grid sampling `'random'` for random coordinate sampling `'pseudo_random'` for random sampling initalized by uniform grids

`grid_spacing:`	x and y grid spacing for sampling	Options: Example : `(5,5)`

`n:`	`random` / `pseudo_random` sampling fraction	Options: Ex. : `0.15`

`masks:`	TIFF mask to compress image portion	Options: Ex. : `'moving-mask.tiff'`

`save_mem:`	reduce memory footprint	Options: `True` for large image compression `False` if interactive Python code

Step : `ProcessingSteps`	steps to process images

`- RunOptimalUMAP`	steady-state compression	Options: `n_neighbors` nearest neighbors (Ex. `15`) `landmarks` spectral centroids (Ex. `3000`) `metric` UMAP metric (Ex. `euclidean`) `random_state` seed (Ex. `1`) `dim_range` range of dimensionalities (Ex. `(1,10)`) `**kwargs` kwargs passed to UMAP

`- RunUMAP`	UMAP compression	`**kwargs` passed to UMAP

`- RunOptimalParametricUMAP`	neural network steady state UMAP	Options: `n_neighbors` nearest neighbors (Ex. `15`) `landmarks` spectral centroids (Ex. `3000`) `metric` UMAP metric (Ex. `euclidean`) `random_state` seed (Ex. `1`) `dim_range` range of dimensionalities (Ex. `(1,10)`) `**kwargs` kwargs passed to UMAP

`- RunParametricUMAP`	neural network UMAP compression	`**kwargs` passed to UMAP

`- SpatiallyMapUMAP`	reconstruct compressed image

`- ApplyManualMask`	apply manual mask	Options: mask accessed from `ImportOptions`

`- MedianFilter`	median filter (remove salt and pepper noise)	Options: `filter_size` filter disk size (Ex. `15`) `parallel` use parallel processing (`True` or `False`)

`- Threshold`	create mask by thresholding	Options: `type` threshold type (`'manual' or 'otsu'`) `thresh_value` manual threshold value (Ex. `1.0`) `correction` multiply threshold for stringent results (Ex. `1.2`)

`- Open`	morphological closing on mask	Options: `disk_size` filter disk size (Ex. `15`) `parallel` use parallel processing (`True` or `False`)

`- Close`	resume workflow with cached results	Options: `disk_size` filter disk size (Ex. `15`) `parallel` use parallel processing (`True` or `False`)

`- Fill`	Fill holes in mask

`- ApplyMask`	apply mask to image for final processing step

`- NonzeroBox`	extract image bounding box (for controlled padding)

Step : `ExportNifti1`	export in the NIfTI format	Options: `padding` pad to add to images image (Ex. `(50,50)`) `target_size` resize image before padding (Ex. `(1000,1050)`)

HDIreg

—elastix-pars
Flag	Description	Options
`--p`	parameter file(s) for registration	`*.txt`
`--mp`	moving image landmark points	`*.txt`
`--fp`	fixed image landmark points	`*.txt`
`--fMask`	fixed image mask	`*.tif`

Tip

You can chain together multiple elastix parameter files by supplying multiple inputs. For example, an affine registration followed by a nonlinear one can be implemented as --p affine.txt nonlinear.txt where affine.txt and nonlinear.txt are your parameter files.

—transformix-pars
Flag	Description	Options
`--tps`	transformation parameter file(s)	`*.txt`
`--target_size`	resize image before padding	(Ex. `(1000,1050)`)
`--pad`	pad to add to images image	(Ex. `(50,50)`)
`--trim`	number of pixels to trim off edges	(Ex. `50`)
`--out_ext`	aligned image final file format	`.ome.tiff` `.ome.tif` `.tiff` `.hdf5` `.h5` `.nii`

Tip

If you use multiple registration parameter files in elastix, then you should add both sets of transformation parameters to the transformix command to receive final results. From the above example, two transformation parameter files would be exported – TransformationParameters.0.txt for the affine registration and TransformationParameters.1.txt for nonlinear. Your transformix call should be --tps TransformationParameters.0.txt TransformationParameters.1.txt