Three-dimensional murine airway segmentation in micro-CT images

Lijun Shi; Jacqueline Thiesse; Geoffrey McLennan; Eric A. Hoffman; Joseph M. Reinhardt

doi:10.1117/12.711213

29 March 2007 Three-dimensional murine airway segmentation in micro-CT images

Lijun Shi, Jacqueline Thiesse, Geoffrey McLennan, Eric A. Hoffman, Joseph M. Reinhardt

Proceedings Volume 6511, Medical Imaging 2007: Physiology, Function, and Structure from Medical Images; 651105 (2007) https://doi.org/10.1117/12.711213
Event: Medical Imaging, 2007, San Diego, CA, United States

Abstract

Thoracic imaging for small animals has emerged as an important tool for monitoring pulmonary disease progression and therapy response in genetically engineered animals. Micro-CT is becoming the standard thoracic imaging modality in small animal imaging because it can produce high-resolution images of the lung parenchyma, vasculature, and airways. Segmentation, measurement, and visualization of the airway tree is an important step in pulmonary image analysis. However, manual analysis of the airway tree in micro-CT images can be extremely time-consuming since a typical dataset is usually on the order of several gigabytes in size. Automated and semi-automated tools for micro-CT airway analysis are desirable. In this paper, we propose an automatic airway segmentation method for in vivo micro-CT images of the murine lung and validate our method by comparing the automatic results to manual tracing. Our method is based primarily on grayscale morphology. The results show good visual matches between manually segmented and automatically segmented trees. The average true positive volume fraction compared to manual analysis is 91.61%. The overall runtime for the automatic method is on the order of 30 minutes per volume compared to several hours to a few days for manual analysis.

Citation Download Citation

Lijun Shi, Jacqueline Thiesse, Geoffrey McLennan, Eric A. Hoffman, and Joseph M. Reinhardt "Three-dimensional murine airway segmentation in micro-CT images", Proc. SPIE 6511, Medical Imaging 2007: Physiology, Function, and Structure from Medical Images, 651105 (29 March 2007); https://doi.org/10.1117/12.711213

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