Computer-assisted radiology and surgeryOriginal investigationsAutomated lung segmentation for thoracic CT: Impact on computer-aided diagnosis1
Section snippets
Core segmentation method
The automated lung segmentation method is depicted in Fig 1. Aspects of this method have been reported previously (2, 10, 30, 31). With the exception of trachea and main bronchi elimination, which makes use of the contiguity of the airways between adjacent sections, each step is performed on a section-by-section basis. The overall method includes core techniques and also allows for the implementation of additional task-specific modifications to the lung segmentation regions.
As visualized on a
Nodule detection results
The automated lung nodule detection method achieved 71% nodule detection sensitivity with an average of 0.4 false-positive detections per section on a database of 38 CT scans (34). The effect of lung segmentation on these results may be appreciated through the observation that of the 82 actual lung nodules in this 38-case database, only 4 nodules (4.9%) were excluded from the lung segmentation regions when the core lung segmentation method was modified by the rolling ball algorithm and the
Conclusion
We have developed automated lung segmentation methods that provide an important part of our CAD research for thoracic CT scans. These methods have been successfully applied to the automated detection of lung nodules and to the computer-assisted measurement of mesothelioma. The present study demonstrates that the lung segmentation method used as preprocessing for CAD applications may affect CAD results. The core lung segmentation method yielded lung regions that excluded 17.1% of the lung
Acknowledgment
The authors would like to thank Heber MacMahon, MD, Masha Kocherginsky, PhD, Maryellen L. Giger, PhD, Nicholas J. Vogelzang, MD, Hedy L. Kindler, MD, Carl J. Vyborny, MD, PhD, John Fennessy, MD, Feng Li, MD, PhD, Geoffrey R. Oxnard, BA, Michael B. Altman, BA, Adam Starkey, and Tamara Thompson, RN, for their assistance.
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Supported in part by USPHS Grant CA83908, a grant from the Mesothelioma Applied Research Foundation, a grant from the American Lung Association of Metropolitan Chicago, and funding from The University of Chicago Cancer Research Center.