By my collaborative efforts with NIH, University of Kentucky, Johns Hopkins University, University of Tennessee, University of Pennsylvania, Howard Hughes Medical Institute as well as Imperial College London (UK) and Institute of Cancer Research in London (UK), the study titled “Computer-aided pulmonary image analysis in small animal models” was recently accepted for publication. The study is particularly important because it proposes a general framework for quantitative analysis of lung diseases in small animal models using CT scans.
Medical Physics is the scientific journal of the American Association of Physicists in Medicine and is an official science journal of the Canadian Organization of Medical Physicists, the Canadian College of Physicists in Medicine, and the International Organization for Medical Physics (IOMP). It publishes research concerned with the application of physics and mathematics to the solution of problems in medicine and human biology. Manuscripts covering theoretical or experimental approaches are published.
Computer-aided pulmonary image analysis in small animal models
Ziyue Xu, Ulas Bagci*, Awais Mansoor, Gabriela Kramer-Marek, Brian Luna, Andre Kubler, Bappaditya Dey, Brent Foster, Georgios Z. Papadakis, Jeremy V. Camp, Colleen B. Jonsson, William R. Bishai, Sanjay Jain, Jayaram K. Udupa, Daniel J. Mollura
*corresponding author, and senior person responsible for the study.
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 diff erence 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 affi nity 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 diff erent 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 coeffi cient > 0. 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.