ArticleScientific data2024
3D whole body preclinical micro-CT database of subcutaneous tumors in mice with annotations from 3 annotators.
Article in Scientific data, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Applications of artificial intelligence in nuclear medicine.Zeitschrift fur medizinische Physik · 2026Review
- High Throughput In vivo Imaging; A Pipeline for Automated Analysis of Longitudinal Preclinical Studies.Current protocols · 2026Article
- Quantitative Preclinical Imaging as a Metrological Framework: Reproducibility, Validation, and Translational Maturity.Journal of imaging · 2026Review
- A MultiRater MultiOrgan Abdominal CT Dataset for Calibration Analysis and Uncertainty Modeling in Segmentation.Scientific data · 2026Article
- Laser-Ablated Gold Nanoparticles as Tunable Contrast Agents for Preclinical Imaging.Nanomaterials (Basel, Switzerland) · 2025Article
- Unveiling Another Dimension: Advanced Visualization of Cancer Invasion and Metastasis via Micro-CT Imaging.Cancers · 2025Article
- 3D whole body preclinical micro-CT database of subcutaneous tumors in mice with annotations from 3 annotators.Scientific data · 2024Article
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Authors and funding
7 authors.
Funding
Abstract
A pivotal animal model for development of anticancer molecules is mice with subcutaneous tumors, grown by injection of xenografted tumor cells, where micro-Computed Tomography (µCT) of the mice is used to analyze the efficacy of the anticancer molecule. Manual delineation of the tumor region is necessary for the analysis, which is time-consuming and inconsistent, highlighting the need for automatic segmentation (AS) tools. This study introduces a preclinical µCT database, comprising 452 whole-body scans from 223 individual mice with subcutaneous tumors, spanning ten diverse µCT datasets conducted between 2014 and 2020 on a preclinical PET/CT scanner, making it the hitherto largest dataset of its kind. Each tumor is annotated manually by three expert annotators, allowing for robust model development. Inter-annotator agreement was analyzed, and we report an overall annotation agreement of 0.903 ± 0.046 (mean ± std) Fleiss' Kappa and a mean deviation in volume estimation of 0.015 ± 0.010 cm
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.