Rapid Automated Target Segmentation and Tracking on 4D Data without Initial Contours. (3rd August 2014)

• Record Type: Journal Article
• Authors: Chebrolu, Venkata V.; Saenz, Daniel; Tewatia, Dinesh; Sethares, William A.; Cannon, George; Paliwal, Bhudatt R.
• Other Names: Bisdas Sotirios Academic Editor.
• Is Part Of: Radiology research and practice. Volume 2014(2014)
• Publication Date: 2014-08-03
• Subjects: Radiography -- Periodicals; Radiology -- Periodicals; Medical radiology -- Periodicals; Radiology; Radiography; Radiology; Medical radiology; Electronic journals; Periodicals; Periodicals; Fulltext; Internet Resources; Periodicals; 616.0757
• DOI: 10.1155/2014/547075
• Languages: English
• ISSNs: 2090-1941

Abstract

Abstract : Purpose . To achieve rapid automated delineation of gross target volume (GTV) and to quantify changes in volume/position of the target for radiotherapy planning using four-dimensional (4D) CT. Methods and Materials . Novel morphological processing and successive localization (MPSL) algorithms were designed and implemented for achieving autosegmentation. Contours automatically generated using MPSL method were compared with contours generated using state-of-the-art deformable registration methods (using Elastix© and MIMVista software). Metrics such as the Dice similarity coefficient, sensitivity, and positive predictive value (PPV) were analyzed. The target motion tracked using the centroid of the GTV estimated using MPSL method was compared with motion tracked using deformable registration methods. Results . MPSL algorithm segmented the GTV in 4DCT images in 27.0 ± 11.1 seconds per phase ( 512 × 512 resolution) as compared to 142.3 ± 11.3 seconds per phase for deformable registration based methods in 9 cases. Dice coefficients between MPSL generated GTV contours and manual contours (considered as ground-truth) were 0.865 ± 0.037 . In comparison, the Dice coefficients between ground-truth and contours generated using deformable registration based methods were 0.909 ± 0.051. Conclusions . The MPSL method achieved similar segmentation accuracy as compared to state-of-the-art deformable registration based segmentation methods, but with significant reduction in time required for GTV segmentation.