segment image

segment vesalius images to find initial territories

Usage

segment_image(
  vesalius_assay,
  dimensions = seq(1, 3),
  embedding = "last",
  method = "kmeans",
  col_resolution = 10,
  compactness = 1,
  scaling = 0.5,
  threshold = 0.9,
  index_selection = "bubble",
  verbose = TRUE
)

Arguments

vesalius_assay

a vesalius_assay object

dimensions

numeric vector of latent space dimensions to use.

embedding

character string describing which embedding should be used.

method

character string for which method should be used for segmentation. Select from "kmeans", "louvain", "leiden", "slic", "leiden_slic","louvain_slic","som"

col_resolution

numeric colour resolution used for segmentation. (see details)

compactness

numeric - factor defining super pixel compaction.

scaling

numeric - scaling image ration during super pixel segmentation.

threshold

numeric [0,1] - correlation threshold between nearest neighbors when generating segments from super pixels.

verbose

logical - progress message output.

k

numeric - number of closest super pixel neighbors to consider when generating segments from super pixels

Value

a vesalius_assay object

Details

Applying image segmentation ensures a reduction in colour complexity.

Vesalius provides 7 different methods for clustering colours and reducing color complexity: **Kmeans**, **Louvain**, **Leiden**, **slic**, **leiden_slic**, **louvain_slic**, and **som**

In the case of kmeans clustering the col_resolution argument shows the number of colours that the images should be reduced to. In this case, col_resolution should be an integer and we suggest first looking at values between 3 and 20.

In the case of **leiden** and **louvain** clustering, the col_resolution is the granularity of the clustering. In this case, we suggest using values between 0.01 and 1 to start with. We recommned uisng **louvain** clustering over **leiden** in this context.

In the case of slic, the col_resolution define the number of starting points used to generate super pixels. Depending on the number of points there are in the assay, we suggested using 10 number of points as starting point. For example, if you have 1000 spatial indices, you can set col_resolution to 100.

The optimal col_resolution will depend on your interest and biological question at hand. You might be interested in more or less granular territories. Along with smoothing, the number of segments is one way to control this granularity.

Examples

if (FALSE) { # \dontrun{
data(vesalius)
# First we build a simple object
ves <- build_vesalius_object(coordinates, counts)
# We can do a simple run
ves <- build_vesalius_embeddings(ves)

# simple smoothing
ves <- smooth_image(ves, dimensions = seq(1, 30))

# quick segmentation
ves <- segment_image(ves, dimensions = seq(1, 30))
} # }