title: Quantitative normal thoracic anatomy at CT.
abstract: Automatic anatomy recognition (AAR) methodologies for a body region require detailed understanding of the morphology, architecture, and geographical layout of the organs within the body region. The aim of this paper was to quantitatively characterize the normal anatomy of the thoracic region for AAR. Contrast-enhanced chest CT images from 41 normal male subjects, each with 11 segmented objects, were considered in this study. The individual objects were quantitatively characterized in terms of their linear size, surface area, volume, shape, CT attenuation properties, inter-object distances, size and shape correlations, size-to-distance correlations, and distance-to-distance correlations. A heat map visualization approach was used for intuitively portraying the associations between parameters. Numerous new observations about object geography and relationships were made. Some objects, such as the pericardial region, vary far less than others in size across subjects. Distance relationships are more consistent when involving an object such as trachea and bronchi than other objects. Considering the inter-object distance, some objects have a more prominent correlation, such as trachea and bronchi, right and left lungs, arterial system, and esophagus. The proposed method provides new, objective, and usable knowledge about anatomy whose utility in building body-wide models toward AAR has been demonstrated in other studies.
authors.affiliation: Medical Image Processing Group, Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, United States.
authors.forename: Monica M S
authors.initials: MM
authors.lastname: Matsumoto
authors.affiliation: Medical Image Processing Group, Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, United States. Electronic address: jay@mail.med.upenn.edu.
authors.forename: Jayaram K
authors.initials: JK
authors.lastname: Udupa
authors.affiliation: Medical Image Processing Group, Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, United States.
authors.forename: Yubing
authors.initials: Y
authors.lastname: Tong
authors.affiliation: Medical Image Processing Group, Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, United States.
authors.forename: Babak
authors.initials: B
authors.lastname: Saboury
authors.affiliation: Medical Image Processing Group, Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, United States.
authors.forename: Drew A
authors.initials: DA
authors.lastname: Torigian
mesh_terms: D006801:Humans; D008297:Male; D008875:Middle Aged; D013902:Radiography, Thoracic; D013909:Thorax / Q000033:anatomy & histology* / Q000000981:diagnostic imaging*; D014057:Tomography, X-Ray Computed
keywords: Automatic anatomy recognition; CT; Quantification; Quantitative radiology; Segmentation; Thorax