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

## TL;DR

This paper introduces a new preclinical liver imaging dataset with MRI and CT scans from mice, annotated for use in research on liver diseases.

## Contribution

The first publicly available preclinical liver imaging dataset combining anatomical, functional, and segmentation data from multiple mouse models.

## Key findings

- The dataset includes MRI and CT scans from mice with hepatocellular carcinoma, MASH, and fibrosis.
- Some scans have annotated segmentations, enabling training of automated image analysis tools.
- Metadata is structured using a tailored ISA-Tab profile for improved data reuse and reproducibility.

## Abstract

Chronic liver diseases (CLD) account for more than 2% of deaths worldwide. Extensive research has been conducted to better understand CLD, generating vast amounts of data. However, only a small fraction of raw preclinical data are publicly available, posing a significant challenge for transparency, reproducibility, and data reuse. Therefore, we built a preclinical liver imaging dataset, the first of its kind to our knowledge. The database contains longitudinal liver MRI scans from mice with hepatocellular carcinoma, metabolic dysfunction-associated steatohepatitis (MASH, formerly NASH), and fibrosis, as well as CT scans of mice with MASH and mice carrying a dysfunctional ICAM-1 gene. Superimposable MRI and CT scans bridge the gap between the modalities. Some of the 222 murine scans have annotated segmentations. Metadata containing both scan and mouse parameters are organized using a tailored metadata profile in ISA-Tabs. This dataset enables advanced image analysis, such as building tools for automated segmentation, train radiomics analysis tools, or can be used as a reference control dataset.