annotatR provides multi-layer region-of-interest (ROI) annotation for whole-slide microscopy images, hyperspectral data cubes, and conventional rasters. Annotations are validated simple-feature geometries stored in image pixel coordinates with explicit pyramid-level transforms, and rasterise to binary, labelled, or multi-class integer masks for downstream segmentation and classification. A batch annotation application supports resumable sessions across image queues, live mask preview, and export to GeoJSON, QuPath, and TIFF.
# install.packages("pak")
pak::pak("CTTIR/annotatR")library(annotatR)
# Read a bundled example image and build a project with one annotation layer.
img <- at_example_image("tissue")
proj <- at_project(img, at_layer("regions", labels = c("tumour", "stroma")))
# Add regions of interest.
proj <- proj |>
at_add_roi("regions", at_roi_rect(120, 150, 260, 290, label = "tumour")) |>
at_add_roi("regions", at_roi_circle(400, 120, 60, label = "stroma"))
at_rois(proj)[, c("roi_id", "label", "area_px")]
#> # A tibble: 2 × 3
#> roi_id label area_px
#> <chr> <chr> <dbl>
#> 1 roi_000000001 tumour 19600
#> 2 roi_000000002 stroma 11292.
# Rasterise to a labelled mask (the "Schablone") and write it with a legend.
mask <- at_mask(proj, type = "labelled")
path <- tempfile(fileext = ".tif")
at_write_mask(mask, path) # also writes <path>.legend.jsonat_plot_overlay(proj)
| Format | Backend | Package required | Pyramid | Spectral |
|---|---|---|---|---|
| PNG / JPEG / TIFF | raster |
magick or tiff | no | no |
| Pyramidal / OME-TIFF | tiff |
tiff | yes | multiplex |
| qptiff, OME-TIFF | ometiff |
RBioFormats | yes | multiplex |
Cubert .cu3 |
cuvis |
cuvis.r | no | yes |
| ENVI cube | envi |
base R | no | yes |
| Diaspective Vision Tivita | tivita |
base R (ENVI) | no | yes |
A mask is the central output artifact: a binary, labelled, or
multi-class integer raster derived from your annotations, exportable at
any pyramid level and self-describing via a sidecar JSON legend. Masks
feed segmentation, classification, and spectral-extraction pipelines in
Python, ImageJ, QuPath, or R. See vignette("masks") for the
pixel-coverage contract and downstream consumption.
# Launch the batch annotation app over a queue of images:
at_annotate(at_example_session(5))Iterate through an image queue, draw multi-layer ROIs, watch the mask render live, and export everything in one pass — with resumable sessions and keyboard-first throughput.
Unlike QuPath, the ImageJ ROI Manager, napari, or Annotorious, annotatR is R-native and script-first, handles hyperspectral cubes alongside microscopy, and treats the batch mask export as a first-class, reproducible artifact.
annotatR builds on OpenSeadragon,
Annotorious, and the
sf and
stars packages.
Portions of this package were prepared with assistance from large language model tooling for narrowly defined, non-authorial tasks: copyediting, prose smoothing, Markdown/LaTeX formatting, scaffolding of boilerplate files (CI configs, build scripts), code refactoring. The tools used were Chat AI, the LLM service of KISSKI (GWDG), and a self-hosted Mistral Small (24B, Apache-2.0) run locally via Ollama and the ollamar R package — local inference only, with no data sent to third parties for the self-hosted model.