A preclinical CT and MRI Liver Imaging Dataset with Anatomical, Functional and Segmentation Data

Area of Science:

• Biomedical imaging
• Data science
• Liver disease research

Background:

• Chronic liver diseases (CLD) cause over 2% of global deaths.
• Vast research data exists, but raw preclinical data access is limited, hindering transparency and reproducibility.
• A public repository for preclinical liver imaging data is needed.

Purpose of the Study:

• To create the first comprehensive preclinical liver imaging dataset.
• To facilitate advanced image analysis and data reuse in liver disease research.
• To improve transparency and reproducibility in preclinical studies.

Main Methods:

• Compilation of longitudinal liver MRI and CT scans from mice.
• Inclusion of scans from mice with hepatocellular carcinoma, metabolic dysfunction-associated steatohepatitis (MASH), and fibrosis.
• Organization of 222 scans with metadata using ISA-Tabs, including some annotated segmentations.

Main Results:

• A novel, publicly available dataset of preclinical liver imaging scans.
• Superimposable MRI and CT scans are included, bridging imaging modalities.
• The dataset contains scans from various liver conditions and includes detailed metadata.

Conclusions:

• This dataset enables advanced analyses, including automated segmentation and radiomics.
• It serves as a valuable resource for researchers studying liver diseases.
• The dataset promotes data sharing, transparency, and reproducibility in preclinical research.