Individual nodule tracking in micro-CT images of a longitudinal lung cancer mouse model Articles uri icon

publication date

  • December 2013

start page

  • 1095

end page

  • 1105


  • 8


  • 17

International Standard Serial Number (ISSN)

  • 1361-8415

Electronic International Standard Serial Number (EISSN)

  • 1361-8423


  • Abstract: We present and evaluate an automatic and quantitative method for the complex task of characterizingindividual nodule volumetric progression in a longitudinal mouse model of lung cancer. Fourteen A/Jmice received an intraperitoneal injection of urethane. Respiratory-gated micro-CT images of the lungswere acquired at 8, 22, and 37 weeks after injection. A radiologist identified a total of 196, 585 and 636 nodules, respectively. The three micro-CT image volumes from every animal were then registeredand the nodules automatically matched with an average accuracy of 99.5%. All nodules detected at week 8 were tracked all the way to week 37, and volumetrically segmented to measure their growth and dou-bling rates. 92.5% of all nodules were correctly segmented, ranging from the earliest stage to advancedstage, where nodule segmentation becomes more challenging due to complex anatomy and nodule over-lap. Volume segmentation was validated using a foam lung phantom with embedded polyethylenemicrospheres. We also correlated growth rates with nodule phenotypes based on histology, to concludethat the growth rate of malignant tumors is significantly higher than that of benign lesions. In conclusion, we present a turnkey solution that combines longitudinal imaging with nodule matching and volumetricnodule segmentation resulting in a powerful tool for preclinical research.


  • small animal imaging; lung cancer; nodule segmentation; nodule matching; microcomputed tomography; polyethylene; accuracy; animal experiment; animal model; article; cancer staging; correlation analysis; growth rate; image analysis; imaging phantom; lung nodule; male; mouse; nonhuman; phenotype; priority journal; algorithms; animals; lung neoplasms; mice; neoplasm invasiveness; pattern recognition; automated; radiographic image interpretation; computer assisted; reproducibility of results; sensitivity; specificity; subtraction technique; tomography; x-ray computed