One row per object. voxels is the count, which means nothing outside one scanner, and spacing turns it into a number a reader can compare - the same argument mask_volume() makes for one class, applied per object. That is the question this whole feature is for: how many nuclei, and how big is each one.
instance_sizes(labels, spacing = NULL)
labels
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An instance label map from predict() - one integer per object, 0 for background. Two dimensions for an image, three for a volume.
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spacing
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The size of one voxel in millimetres: two numbers for an image, three for a volume. Unset means the result counts voxels and says nothing about size. |
The unit is decided by the rank, and the column is named after it rather than being called size with a note somewhere: area_mm2 for an image, volume_ml for a volume. A column whose unit lives in the documentation is a column that ends up in a paper as a bare number.
Without spacing there is no such column at all, rather than a count pretending to be a measurement.
A data frame with object, voxels and, when spacing is given, area_mm2 or volume_ml. One row per object present, in increasing object number; an object number absent from the map gets no row, because a model that found objects 1, 2 and 4 found three objects.
overlay_instances() to look at them, mask_volume() for one class of a semantic mask.
library("rplatypus")
labels <- matrix(0L, nrow = 8, ncol = 8)
labels[2:4, 2:4] <- 1L
labels[6:7, 6:8] <- 2L
instance_sizes(labels) object voxels
1 1 9
2 2 6# 0.5 mm pixels: object 1 is 9 pixels, so 9 * 0.25 = 2.25 mm^2.
instance_sizes(labels, spacing = c(0.5, 0.5)) object voxels area_mm2
1 1 9 2.25
2 2 6 1.50