title: Lung segmentation from CT with severe pathologies using anatomical constraints.
abstract: The diversity in appearance of diseased lung tissue makes automatic segmentation of lungs from CT with severe pathologies challenging. To overcome this challenge, we rely on contextual constraints from neighboring anatomies to detect and segment lung tissue across a variety of pathologies. We propose an algorithm that combines statistical learning with these anatomical constraints to seek a segmentation of the lung consistent with adjacent structures, such as the heart, liver, spleen, and ribs. We demonstrate that our algorithm reduces the number of failed detections and increases the accuracy of the segmentation on unseen test cases with severe pathologies.
authors.forename: Neil
authors.initials: N
authors.lastname: Birkbeck
authors.forename: Timo
authors.initials: T
authors.lastname: Kohlberger
authors.forename: Jingdan
authors.initials: J
authors.lastname: Zhang
authors.forename: Michal
authors.initials: M
authors.lastname: Sofka
authors.forename: Jens
authors.initials: J
authors.lastname: Kaftan
authors.forename: Dorin
authors.initials: D
authors.lastname: Comaniciu
authors.forename: S Kevin
authors.initials: SK
authors.lastname: Zhou
mesh_terms: D000465:Algorithms; D059925:Anatomic Landmarks / Q000000981:diagnostic imaging*; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008168:Lung / Q000000981:diagnostic imaging*; D008171:Lung Diseases / Q000000981:diagnostic imaging*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014057:Tomography, X-Ray Computed / Q000379:methods*