Abstract
The gold-standard of preclinical micro-computed tomography (μCT) data processing is still manual delineation of complete organs or regions by specialists. However, this method is time-consuming, error-prone, has limited reproducibility, and therefore is not suitable for large-scale data analysis. Unfortunately, robust and accurate automated whole body segmentation algorithms are still missing. In this publication, we introduce a database containing 225 murine 3D whole body μCT scans along with manual organ segmentation of most important organs including heart, liver, lung, trachea, spleen, kidneys, stomach, intestine, bladder, thigh muscle, bone, as well as subcutaneous tumors. The database includes native and contrast-enhanced, regarding spleen and liver, μCT data. All scans along with organ segmentation are freely accessible at the online repository Figshare. We encourage researchers to reuse the provided data to evaluate and improve methods and algorithms for accurate automated organ segmentation which may reduce manual segmentation effort, increase reproducibility, and even reduce the number of required laboratory animals by reducing a source of variability and having access to a reliable reference group.
Highlights
Background & SummaryMicro-computed tomography is one of the most commonly used imaging technologies in preclinical research
The main drawback of μCT imaging is a low soft tissue contrast, which can be improved by the utilization of radiopaque contrast agents[19,20]
Our open-access database includes 225 native and contrast-enhanced whole-animal μCT volumes along with manual organ segmentations acquired from mice scanned longitudinally in different positions
Summary
Micro-computed tomography (μCT) is one of the most commonly used imaging technologies in preclinical research. Our open-access database includes 225 native and contrast-enhanced whole-animal μCT volumes along with manual organ segmentations acquired from mice scanned longitudinally in different positions. Organ parameters such as volume, surface, and distances in one individual remain stable over time. We highly encourage researchers to use these 3D datasets, e.g. for further comparative analysis of organ morphology or to determine relevant μCT features such as intensity or variations between voxels This introduced database will be used to validate segmentation and machine-learning approaches and facilitate the development of reliable, simplified, and user-independent analysis tools for whole body organ segmentation. The anatomical 3D data of the whole mouse body including the main organs will serve as a visual and education resource to train researchers for segmentation of tumors and organs
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