Paper

{
  "raw_xml": "<entry>\n    <id>http://arxiv.org/abs/2603.12016v1</id>\n    <title>Nyxus: A Next Generation Image Feature Extraction Library for the Big Data and AI Era</title>\n    <updated>2026-03-12T14:58:07Z</updated>\n    <link href='https://arxiv.org/abs/2603.12016v1' rel='alternate' type='text/html'/>\n    <link href='https://arxiv.org/pdf/2603.12016v1' rel='related' title='pdf' type='application/pdf'/>\n    <summary>Modern imaging instruments can produce terabytes to petabytes of data for a single experiment. The biggest barrier to processing big image datasets has been computational, where image analysis algorithms often lack the efficiency needed to process such large datasets or make tradeoffs in robustness and accuracy. Deep learning algorithms have vastly improved the accuracy of the first step in an analysis workflow (region segmentation), but the expansion of domain specific feature extraction libraries across scientific disciplines has made it difficult to compare the performance and accuracy of extracted features. To address these needs, we developed a novel feature extraction library called Nyxus. Nyxus is designed from the ground up for scalable out-of-core feature extraction for 2D and 3D image data and rigorously tested against established standards. The comprehensive feature set of Nyxus covers multiple biomedical domains including radiomics and cellular analysis, and is designed for computational scalability across CPUs and GPUs. Nyxus has been packaged to be accessible to users of various skill sets and needs: as a Python package for code developers, a command line tool, as a Napari plugin for low to no-code users or users that want to visualize results, and as an Open Container Initiative (OCI) compliant container that can be used in cloud or super-computing workflows aimed at processing large data sets. Further, Nyxus enables a new methodological approach to feature extraction allowing for programmatic tuning of many features sets for optimal computational efficiency or coverage for use in novel machine learning and deep learning applications.</summary>\n    <category scheme='http://arxiv.org/schemas/atom' term='cs.CV'/>\n    <category scheme='http://arxiv.org/schemas/atom' term='q-bio.QM'/>\n    <published>2026-03-12T14:58:07Z</published>\n    <arxiv:comment>29 pages, 9 figures, 6 supplemental tables</arxiv:comment>\n    <arxiv:primary_category term='cs.CV'/>\n    <author>\n      <name>Nicholas Schaub</name>\n    </author>\n    <author>\n      <name>Andriy Kharchenko</name>\n    </author>\n    <author>\n      <name>Hamdah Abbasi</name>\n    </author>\n    <author>\n      <name>Sameeul Samee</name>\n    </author>\n    <author>\n      <name>Hythem Sidky</name>\n    </author>\n    <author>\n      <name>Nathan Hotaling</name>\n    </author>\n  </entry>"
}