Cardiac atlas development and validation for automatic segmentation of cardiac substructures. Issue 1 (January 2017)

• Record Type: Journal Article
• Authors: Zhou, Rongrong; Liao, Zhongxing; Pan, Tinsu; Milgrom, Sarah A.; Pinnix, Chelsea C.; Shi, Anhui; Tang, Linglong; Yang, Ju; Liu, Ying; Gomez, Daniel; Nguyen, Quynh-Nhu; Dabaja, Bouthaina S.; Court, Laurence; Yang, Jinzhong
• Is Part Of: Radiotherapy and oncology. Volume 122:Issue 1(2017:Jan.)
• Page Start: 66
• Page End: 71
• Publication Date: 2017-01
• Subjects: Cardiac atlas -- Cutomatic contouring -- Multi-atlas segmentation -- Radiotherapy; Oncology -- Periodicals; Radiotherapy -- Periodicals; Tumors -- Periodicals; Medical Oncology -- Periodicals; Neoplasms -- radiotherapy -- Periodicals; Radiotherapy -- Periodicals; Radiothérapie -- Périodiques; Cancérologie -- Périodiques; Tumeurs -- Périodiques; Electronic journals; 616.9940642
• DOI: 10.1016/j.radonc.2016.11.016
• Languages: English
• ISSNs: 0167-8140

Abstract

Abstract: Purpose: To develop and validate a set of atlases for auto-contouring cardiac substructures. Methods: Eight radiation oncologists manually and independently delineated 15 cardiac substructures from noncontrast CT images of 6 patients by referring to their respective fused contrast CT images. Individual contours were fused together for each structure, edited by 2 physicians, and became atlases to delineate other 6 patients. The auto-delineated contours of the 6 additional patients became templates for manual contouring. These 12 patients with well-defined contours composed the final atlases for multi-atlas segmentation. Results: The average time for manually contouring the 15 cardiac substructures was about 40 min. Inter-observer variability was small for the heart, the chambers, and the aorta compared with that for other structures that were not clearly distinguishable in CT images. The mean dice similarity coefficient and mean surface distance of auto-segmented contours were within one standard deviation of expert contouring variability. Good agreement between auto-segmented and manual contours was observed for the heart, the chambers, and the great vessels. Independent validation on other 19 patients showed reasonable agreement for the heart chambers. Conclusions: A set of cardiac atlases was created for auto-contouring from noncontrast CT images. The accuracy of auto-contouring for the heart, chambers, and great vessels was validated for potential clinical use.