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Learning-based segmentation using star-convex object representations, with workflows for microscopy images. Suitability depends on object shapes, training data and image dimensions.
Learning-based segmentation using star-convex object representations, with workflows for microscopy images. Suitability depends on object shapes, training data and image dimensions.
信息已比对权威文献或官方白皮书；非实验性跑分。 权威出处 · 最后核对: 14/09/2026, 18:31:56 UTC
Use this citation format when referencing StarDist in scientific publications and benchmark papers.
@software{stardist_2026,
title = {{StarDist}},
author = {{StarDist contributors}},
year = {2026},
url = {https://github.com/stardist/stardist},
note = {Indexed on aibioatlas - AI for Biology and Drug Discovery}
}Live scientific citations queried from Europe PMC and PubMed for StarDist.
Missing values mean the catalog has no recorded information. They do not mean a feature is absent.
Project documentation; editorial evaluation plan · Compare these alternative analysis tools on the same images; no cross-tool interoperability is asserted.
StarDist Scientific Architecture · Credit: StarDist contributors · Open original ↗
Research and editorial use Source & attribution ↗ License ↗
同一科学领域的同类算法与工具。
Classical/statistical scientific method · Confirm distribution and exact release
CellProfiler — cell-segmentation / classical-microscopy-measurement
Research / method authors
Source-scoped research method · Confirm distribution and exact release
H-Optimus-0 — pathology-tile-embedding
Research / method authors
暂无与此工具关联的已验证学术动态。
浏览全部生物AI动态 →Public code
费用根据官方最近一次公布的说明整理。请在官方网站确认最新报价与学术折扣。
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