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cff-version: 1.2.0
message: 'To cite package "segmantR" in publications use:'
type: software
license: MIT
title: 'segmantR: Cell Segmentation for Histology with Human-in-the-Loop Training'
version: 0.2.0
abstract: Cell segmentation for histology and multiplexed tissue imaging. Provides
  classical segmentation methods (adaptive thresholding, watershed, Voronoi propagation),
  wrappers through 'reticulate' for the deep learning models 'Cellpose' (Stringer
  et al. (2021) <https://doi.org/10.1038/s41592-020-01018-x>), 'StarDist' (Schmidt
  et al. (2018) <https://doi.org/10.1007/978-3-030-00934-2_30>) and 'Mesmer' (Greenwald
  et al. (2022) <https://doi.org/10.1038/s41587-021-01094-0>), and a human-in-the-loop
  iterative refinement workflow. A 'shiny' application lets researchers annotate,
  correct, train, evaluate and download custom segmentation models without leaving
  R. Versioned declarative segmentation protocols, a neutral interchange format (JSON
  manifests, integer label TIFF, GeoJSON, measurement tables with SHA-256 inventories),
  training dataset manifests with grouped splits, and readable 'QuPath' script templates
  with a 'StarDist' parameter adapter support reproducible exchange with 'QuPath'
  and partner packages.
authors:
- family-names: Heller
  given-names: R.
  email: raban.heller@uni-ulm.de
  orcid: https://orcid.org/0000-0001-8006-9742
repository: https://cttir.r-universe.dev
repository-code: https://github.com/CTTIR/segmantR
commit: 882c2fb7fe0546f71f6f11bbfd3de6103859b098
url: https://cttir.github.io/segmantR/
date-released: '2026-10-29'
contact:
- family-names: Heller
  given-names: R.
  email: raban.heller@uni-ulm.de
  orcid: https://orcid.org/0000-0001-8006-9742