Body region localization in whole‐body low‐dose CT images of PET/CT scans using virtual landmarks. Issue 3 (24th January 2019)

• Record Type: Journal Article
• Authors: Bai, Peirui; Udupa, Jayaram K.; Tong, Yubing; Xie, ShiPeng; Torigian, Drew A.
• Is Part Of: Medical physics. Volume 46:Issue 3(2019)
• Page Start: 1286
• Page End: 1299
• Publication Date: 2019-01-24
• Subjects: body region identification -- computed tomography (CT) -- neural network learning -- principal component analysis -- virtual landmarks; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.13376
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract : Purpose: Radiological imaging and image interpretation for clinical decision making are mostly specific to each body region such as head and neck, thorax, abdomen, pelvis, and extremities. In this study, we present a new solution to trim automatically the given axial image stack into image volumes satisfying the given body region definition. Methods: The proposed approach consists of the following steps. First, a set of reference objects is selected and roughly segmented. Virtual landmarks (VLs) for the objects are then identified by using principal component analysis and recursive subdivision of the object via the principal axes system. The VLs can be defined based on just the binary objects or objects with gray values also considered. The VLs may lie anywhere with respect to the object, inside or outside, and rarely on the object surface, and are tethered to the object. Second, a classic neural network regressor is configured to learn the geometric mapping relationship between the VLs and the boundary locations of each body region. The trained network is then used to predict the locations of the body region boundaries. In this study, we focus on three body regions — thorax, abdomen, and pelvis, and predict their superior and inferior axial locations denoted by TS ( I ), TI ( I ), AS ( I ), AI ( I ), PS ( I ), and PI ( I ), respectively, for any given volume image I . Two kinds of reference objects — the skeleton and the lungs and airways, are employed to test the localization performance of the proposed approach. Results: Our method is tested by using low‐dose unenhanced computed tomography (CT) images of 180 near whole‐body 18 F‐fluorodeoxyglucose‐positron emission tomography/computed tomography (FDG‐PET/CT) scans (including 34 whole‐body scans) which are randomly divided into training and testing sets with a ratio of 85%:15%. The procedure is repeated six times and three times for the case of lungs and skeleton, respectively, with different divisions of the entire data set at this proportion. For the case of using skeleton as a reference object, the overall mean localization error for the six locations expressed as number of slices ( nS ) and distance ( dS ) in mm, is found to be nS : 3.4, 4.7, 4.1, 5.2, 5.2, and 3.9; dS : 13.4, 18.9, 16.5, 20.8, 20.8, and 15.5 mm for binary objects; nS : 4.1, 5.7, 4.3, 5.9, 5.9, and 4.0; dS : 16.2, 22.7, 17.2, 23.7, 23.7, and 16.1 mm for gray objects, respectively. For the case of using lungs and airways as a reference object, the corresponding results are, nS : 4.0, 5.3, 4.1, 6.9, 6.9, and 7.4; dS : 15.0, 19.7, 15.3, 26.2, 26.2, and 27.9 mm for binary objects; nS : 3.9, 5.4, 3.6, 7.2, 7.2, and 7.6; dS : 14.6, 20.1, 13.7, 27.3, 27.3, and 28.6 mm for gray objects, respectively. Conclusions: Precise body region identification automatically in whole‐body or body region tomographic images is vital for numerous medical image analysis and analytics applications. Despite its importance, this issue has received very little attention in the literature. We present a solution to this problem in this study using the concept of virtual landmarks. The method achieves localization accuracy within 2–3 slices, which is roughly comparable to the variation found in localization by experts. As long as the reference objects can be roughly segmented, the method with its learned VLs‐to‐boundary location relationship and predictive ability is transferable from one image modality to another.