A Differential Geometric Approach to Automated Segmentation of Human Airway Tree

• Published in: IEEE transactions on medical imaging Vol. 30; no. 2; pp. 266 - 278
• Main Authors: Jiantao Pu, Fuhrman, Carl, Good, Walter F, Sciurba, Frank C, Gur, David
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.02.2011; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Airway tree; Airways; Algorithms; Anatomical structure; Atmospheric modeling; Bronchi - anatomy & histology; Bronchography - methods; Computed tomography; computer-aided detection; Curvature; Diagnosis, Computer-Assisted; Differential geometry; Diseases; Humans; Image Processing, Computer-Assisted - methods; Image segmentation; lung computed tomography (CT); Lungs; Pulmonary Disease, Chronic Obstructive - diagnostic imaging; Segmentation; Shape; Smoothing methods; Tomography, X-Ray Computed - methods; Trees
• DOI: 10.1109/TMI.2010.2076300
• ISSN: 0278-0062; 1558-254X

Airway diseases are frequently associated with morphological changes that may affect the physiology of the lungs. Accurate characterization of airways may be useful for quantitatively assessing prognosis and for monitoring therapeutic efficacy. The information gained may also provide insight into the underlying mechanisms of various lung diseases. We developed a computerized scheme to automatically segment the 3-D human airway tree depicted on computed tomography (CT) images. The method takes advantage of both principal curvatures and principal directions in differentiating airways from other tissues in geometric space. A "puzzle game" procedure is used to identify false negative regions and reduce false positive regions that do not meet the shape analysis criteria. The negative impact of partial volume effects on small airway detection is partially alleviated by repeating the developed differential geometric analysis on lung anatomical structures modeled at multiple iso-values (thresholds). In addition to having advantages, such as full automation, easy implementation and relative insensitivity to image noise and/or artifacts, this scheme has virtually no leakage issues and can be easily extended to the extraction or the segmentation of other tubular type structures (e.g., vascular tree). The performance of this scheme was assessed quantitatively using 75 chest CT examinations acquired on 45 subjects with different slice thicknesses and using 20 publicly available test cases that were originally designed for evaluating the performance of different airway tree segmentation algorithms.