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Med Phys. 2015 Jul;42(7):3896-910. doi: 10.1118/1.4921618.

Computer-aided pulmonary image analysis in small animal models.

Author information

1
Center for Infectious Disease Imaging (CIDI), Radiology and Imaging Sciences, National Institutes of Health (NIH), Bethesda, Maryland 32892.
2
Center for Research in Computer Vision (CRCV), University of Central Florida (UCF), Orlando, Florida 32816.
3
The Institute of Cancer Research, London SW7 3RP, United Kingdom.
4
Microfluidic Laboratory Automation, University of California-Irvine, Irvine, California 92697-2715.
5
Department of Medicine, Imperial College London, London SW7 2AZ, United Kingdom.
6
Center for Tuberculosis Research, Johns Hopkins University School of Medicine, Baltimore, Maryland 21231.
7
Department of Biomedical Engineering, University of California-Davis, Davis, California 95817.
8
Radiology and Imaging Sciences, National Institutes of Health (NIH), Bethesda, Maryland 32892.
9
Department of Microbiology and Immunology, University of Louisville, Louisville, Kentucky 40202.
10
National Institute for Mathematical and Biological Synthesis, University of Tennessee, Knoxville, Tennessee 37996.
11
Howard Hughes Medical Institute, Chevy Chase, Maryland 20815 and Center for Tuberculosis Research, Johns Hopkins University School of Medicine, Baltimore, Maryland 21231.
12
Medical Image Processing Group, Department of Radiology, University of Pennsylvania, Philadelphia, Pennsylvania 19104.

Abstract

PURPOSE:

To develop an automated pulmonary image analysis framework for infectious lung diseases in small animal models.

METHODS:

The authors describe a novel pathological lung and airway segmentation method for small animals. The proposed framework includes identification of abnormal imaging patterns pertaining to infectious lung diseases. First, the authors' system estimates an expected lung volume by utilizing a regression function between total lung capacity and approximated rib cage volume. A significant difference between the expected lung volume and the initial lung segmentation indicates the presence of severe pathology, and invokes a machine learning based abnormal imaging pattern detection system next. The final stage of the proposed framework is the automatic extraction of airway tree for which new affinity relationships within the fuzzy connectedness image segmentation framework are proposed by combining Hessian and gray-scale morphological reconstruction filters.

RESULTS:

133 CT scans were collected from four different studies encompassing a wide spectrum of pulmonary abnormalities pertaining to two commonly used small animal models (ferret and rabbit). Sensitivity and specificity were greater than 90% for pathological lung segmentation (average dice similarity coefficient >‚ÄČ0.9). While qualitative visual assessments of airway tree extraction were performed by the participating expert radiologists, for quantitative evaluation the authors validated the proposed airway extraction method by using publicly available EXACT'09 data set.

CONCLUSIONS:

The authors developed a comprehensive computer-aided pulmonary image analysis framework for preclinical research applications. The proposed framework consists of automatic pathological lung segmentation and accurate airway tree extraction. The framework has high sensitivity and specificity; therefore, it can contribute advances in preclinical research in pulmonary diseases.

PMID:
26133591
PMCID:
PMC4464065
DOI:
10.1118/1.4921618
[Indexed for MEDLINE]
Free PMC Article

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