title: Segmentation and Image Analysis of Abnormal Lungs at CT: Current Approaches, Challenges, and Future Trends.
abstract: The computer-based process of identifying the boundaries of lung from surrounding thoracic tissue on computed tomographic (CT) images, which is called segmentation, is a vital first step in radiologic pulmonary image analysis. Many algorithms and software platforms provide image segmentation routines for quantification of lung abnormalities; however, nearly all of the current image segmentation approaches apply well only if the lungs exhibit minimal or no pathologic conditions. When moderate to high amounts of disease or abnormalities with a challenging shape or appearance exist in the lungs, computer-aided detection systems may be highly likely to fail to depict those abnormal regions because of inaccurate segmentation methods. In particular, abnormalities such as pleural effusions, consolidations, and masses often cause inaccurate lung segmentation, which greatly limits the use of image processing methods in clinical and research contexts. In this review, a critical summary of the current methods for lung segmentation on CT images is provided, with special emphasis on the accuracy and performance of the methods in cases with abnormalities and cases with exemplary pathologic findings. The currently available segmentation methods can be divided into five major classes: (a) thresholding-based, (b) region-based, (c) shape-based, (d) neighboring anatomy-guided, and (e) machine learning-based methods. The feasibility of each class and its shortcomings are explained and illustrated with the most common lung abnormalities observed on CT images. In an overview, practical applications and evolving technologies combining the presented approaches for the practicing radiologist are detailed.
authors.affiliation: From the Center for Infectious Disease Imaging, Department of Radiology and Imaging Sciences, National Institutes of Health, Bethesda, Md.
authors.forename: Awais
authors.initials: A
authors.lastname: Mansoor
authors.affiliation: From the Center for Infectious Disease Imaging, Department of Radiology and Imaging Sciences, National Institutes of Health, Bethesda, Md.
authors.forename: Ulas
authors.initials: U
authors.lastname: Bagci
authors.affiliation: From the Center for Infectious Disease Imaging, Department of Radiology and Imaging Sciences, National Institutes of Health, Bethesda, Md.
authors.forename: Brent
authors.initials: B
authors.lastname: Foster
authors.affiliation: From the Center for Infectious Disease Imaging, Department of Radiology and Imaging Sciences, National Institutes of Health, Bethesda, Md.
authors.forename: Ziyue
authors.initials: Z
authors.lastname: Xu
authors.affiliation: From the Center for Infectious Disease Imaging, Department of Radiology and Imaging Sciences, National Institutes of Health, Bethesda, Md.
authors.forename: Georgios Z
authors.initials: GZ
authors.lastname: Papadakis
authors.affiliation: From the Center for Infectious Disease Imaging, Department of Radiology and Imaging Sciences, National Institutes of Health, Bethesda, Md.
authors.forename: Les R
authors.initials: LR
authors.lastname: Folio
authors.affiliation: From the Center for Infectious Disease Imaging, Department of Radiology and Imaging Sciences, National Institutes of Health, Bethesda, Md.
authors.forename: Jayaram K
authors.initials: JK
authors.lastname: Udupa
authors.affiliation: From the Center for Infectious Disease Imaging, Department of Radiology and Imaging Sciences, National Institutes of Health, Bethesda, Md.
authors.forename: Daniel J
authors.initials: DJ
authors.lastname: Mollura
mesh_terms: D005544:Forecasting; D006801:Humans; D008168:Lung / Q000000981:diagnostic imaging*; D008171:Lung Diseases / Q000000981:diagnostic imaging*; D010363:Pattern Recognition, Automated / Q000639:trends*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D013902:Radiography, Thoracic / Q000639:trends; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique / Q000639:trends; D014057:Tomography, X-Ray Computed / Q000639:trends*