Paper
9 March 2010 Semi-automated method to measure pneumonia severity in mice through computed tomography (CT) scan analysis
Ansh Johri, Daniel Schimel, Audrey Noguchi, Lewis L. Hsu
Author Affiliations +
Abstract
Imaging is a crucial clinical tool for diagnosis and assessment of pneumonia, but quantitative methods are lacking. Micro-computed tomography (micro CT), designed for lab animals, provides opportunities for non-invasive radiographic endpoints for pneumonia studies. HYPOTHESIS: In vivo micro CT scans of mice with early bacterial pneumonia can be scored quantitatively by semiautomated imaging methods, with good reproducibility and correlation with bacterial dose inoculated, pneumonia survival outcome, and radiologists' scores. METHODS: Healthy mice had intratracheal inoculation of E. coli bacteria (n=24) or saline control (n=11). In vivo micro CT scans were performed 24 hours later with microCAT II (Siemens). Two independent radiologists scored the extent of airspace abnormality, on a scale of 0 (normal) to 24 (completely abnormal). Using the Amira 5.2 software (Mercury Computer Systems), a histogram distribution of voxel counts between the Hounsfield range of -510 to 0 was created and analyzed, and a segmentation procedure was devised. RESULTS: A t-test was performed to determine whether there was a significant difference in the mean voxel value of each mouse in the three experimental groups: Saline Survivors, Pneumonia Survivors, and Pneumonia Non-survivors. It was found that the voxel count method was able to statistically tell apart the Saline Survivors from the Pneumonia Survivors, the Saline Survivors from the Pneumonia Non-survivors, but not the Pneumonia Survivors vs. Pneumonia Non-survivors. The segmentation method, however, was successfully able to distinguish the two Pneumonia groups. CONCLUSION: We have pilot-tested an evaluation of early pneumonia in mice using micro CT and a semi-automated method for lung segmentation and scoring system. Statistical analysis indicates that the system is reliable and merits further evaluation.
© (2010) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Ansh Johri, Daniel Schimel, Audrey Noguchi, and Lewis L. Hsu "Semi-automated method to measure pneumonia severity in mice through computed tomography (CT) scan analysis", Proc. SPIE 7624, Medical Imaging 2010: Computer-Aided Diagnosis, 76241R (9 March 2010); https://doi.org/10.1117/12.843907
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KEYWORDS
Computed tomography

Lung

In vivo imaging

Bacteria

Computing systems

Image analysis

Statistical analysis

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