Computer‐aided pulmonary image analysis in small animal models. Issue 7 (10th June 2015)

• Record Type: Journal Article
• Authors: Xu, Ziyue; Bagci, Ulas; Mansoor, Awais; Kramer‐Marek, Gabriela; Luna, Brian; Kubler, Andre; Dey, Bappaditya; Foster, Brent; Papadakis, Georgios Z.; Camp, Jeremy V.; Jonsson, Colleen B.; Bishai, William R.; Jain, Sanjay; Udupa, Jayaram K.; Mollura, Daniel J.
• Is Part Of: Medical physics. Volume 42:Issue 7(2015)
• Page Start: 3896
• Page End: 3910
• Publication Date: 2015-06-10
• Subjects: computerised tomography -- fuzzy systems -- Hessian matrices -- image segmentation -- learning (artificial intelligence) -- lung -- medical image processing -- trees (mathematics); Computed tomography -- Segmentation -- Neural networks, fuzzy logic, artificial intelligence -- Matrix theory; Computerised tomographs -- Biological material, e.g. blood, urine; Haemocytometers -- Digital computing or data processing equipment or methods, specially adapted for specific applications -- Image data processing or generation, in general; lung segmentation -- CT -- fuzzy connectedness -- machine learning -- airway lumen segmentation -- small animal model; Lungs -- Pathology -- Computed tomography -- Animals -- Comparative animal models -- Image analysis -- Medical image segmentation -- Cavitation -- Diseases and conditions; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1118/1.4921618
• Languages: English
• ISSNs: 0094-2405

Abstract

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 > 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.