Authors
Steinkamp, R., Maier, H., Hrabe de Angelis, M., Schneltzer, E.
Abstract
The International Mouse Phenotyping Consortium (IMPC) maintains a large archive of whole-body dorsoventral radiographs. However, heterogeneity in file formats, metadata completeness, orientation, and image quality limits its reuse for large-scale quantitative analysis. We present FERDINAND (Framework for Extracting and Restructuring Distributed IMPC radiographs into a Normalised, AI-ready Navigable Dataset), an open framework for programmatic access, harmonisation, quality control, and curation of IMPC radiographs. FERDINAND combines metadata retrieval and local radiograph archiving with centre-aware preprocessing, automated exposure screening, orientation standardisation, and clustering-assisted visual curation. Applied to IMPC Data Release 24, FERDINAND organises 104,819 radiographs acquired between February 2012 and June 2025 across ten phenotyping centres and 6,306 single-gene knockout mouse lines, with semi-automated curation recorded for every image. As an independent validation of the harmonised output, a DeepLabCut-based bone-morphometry pipeline showed that FERDINAND-processed radiographs improve data efficiency for landmark detection: at reduced training-set sizes, they achieve better performance than original radiographs and require fewer manual annotations to reach a given level of quality. FERDINAND provides a reproducible foundation for artificial intelligence (AI)-ready analysis and scalable quantitative phenotyping of heterogeneous IMPC and comparable radiograph resources. The complete FERDINAND source code is openly available on GitHub.
Preprint server:
bioRxiv
The authors list and abstract were imported from bioRxiv on 16 Oct 2026.
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